import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import (Input, Dense, Reshape, Conv2DTranspose, BatchNormalization,
                                     Activation, Conv2D, LeakyReLU, Dropout, Flatten)
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.optimizers import Adam

import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)

df = pd.read_csv('emnist-letters-train.csv')

# Check how many duplicate rows exist
df.duplicated().sum()

9465

df.drop_duplicates(inplace=True)

labels = df.iloc[:, 0]
images = df.iloc[:, 1:].values

df.shape

(55363, 785)

df.head()

df.shape

(55363, 785)

df.describe()

print("Unique labels:", sorted(labels.unique()))

Unique labels: [-2, -1, 1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 16, 17, 20, 24, 26]

import matplotlib.pyplot as plt

# Count occurrences and sort by frequency (descending)
label_counts = labels.value_counts().sort_values(ascending=False)

# Create horizontal bar chart
plt.figure(figsize=(10, 6))
bars = plt.barh(label_counts.index.astype(str), label_counts.values, color=plt.cm.tab20.colors)

# Add count values next to bars
for bar, value in zip(bars, label_counts.values):
    plt.text(value + 50, bar.get_y() + bar.get_height()/2,
             str(value), va='center', fontsize=9)

plt.title("Label Distribution (Descending by Frequency)", fontsize=14, fontweight='bold')
plt.xlabel("Count")
plt.ylabel("Label")
plt.gca().invert_yaxis()  # Highest count at the top
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

label_counts = labels.value_counts().sort_index()
print(label_counts)

24
-2      256
-1      521
 1     3396
 2     3396
 4     3398
 5     3437
 6     3393
 7     3385
 9     3428
 10    3402
 12    3415
 14    3365
 15    3408
 16    3430
 17    3435
 20    3436
 24    3436
 26    3426
Name: count, dtype: int64

# Helper function to plot images by class
def plot_class_images(class_label, n=5):
    indices = df[df.iloc[:, 0] == class_label].index[:n]
    plt.figure(figsize=(10, 2))
    for i, idx in enumerate(indices):
        img = images[idx].reshape(28, 28)
        plt.subplot(1, n, i + 1)
        plt.imshow(img, cmap='gray')
        plt.title(f"Label: {class_label}")
        plt.axis('off')
    plt.tight_layout()
    plt.show()

plot_class_images(-2)
plot_class_images(-1)
plot_class_images(1)
plot_class_images(2)
plot_class_images(4)
plot_class_images(5)
plot_class_images(6)

df = df[~labels.isin([-1, -2])].copy()
df.reset_index(drop=True, inplace=True)

labels = df.iloc[:, 0]
images = df.iloc[:, 1:].values

print("Unique labels after cleaning:", sorted(labels.unique()))

Unique labels after cleaning: [1, 2, 4, 5, 6, 7, 9, 10, 12, 14, 15, 16, 17, 20, 24, 26]

# Map original labels to 0–15
unique_labels = sorted(labels.unique())
label_map = {original: new for new, original in enumerate(unique_labels)}
df['mapped_label'] = df.iloc[:, 0].map(label_map)
mapped_labels = df['mapped_label'].values

# Helper function to plot images using re-mapped label (0–15)
def plot_mapped_class_images(mapped_class_label, n=5):
    indices = df[df['mapped_label'] == mapped_class_label].index[:n]
    plt.figure(figsize=(10, 2))
    for i, idx in enumerate(indices):
        img = images[idx].reshape(28, 28)
        plt.subplot(1, n, i + 1)
        plt.imshow(img.T, cmap='gray')  # Transpose to fix orientation
        plt.title(f"Mapped Label: {mapped_class_label}")
        plt.axis('off')
    plt.tight_layout()
    plt.show()

# Plot samples from re-mapped labels 0 to 5 (i.e., A to F)
for i in range(6):
    plot_mapped_class_images(i)

print("Unique labels after mapping:", sorted(np.unique(mapped_labels))) 

# Count occurrences using the updated labels
label_counts = pd.Series(mapped_labels).value_counts().sort_values(ascending=False)

# Plot as horizontal bar chart
plt.figure(figsize=(10, 6))
bars = plt.barh(label_counts.index.astype(str), label_counts.values, color=plt.cm.tab20.colors)

# Add count values next to bars
for bar, value in zip(bars, label_counts.values):
    plt.text(value + 50, bar.get_y() + bar.get_height()/2,
             str(value), va='center', fontsize=9)

plt.title("Count of Each Updated Label (Descending)", fontsize=14, fontweight='bold')
plt.xlabel("Count")
plt.ylabel("Label")
plt.gca().invert_yaxis()  # So highest count is at the top
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

Unique labels after mapping: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]

import numpy as np
import matplotlib.pyplot as plt


images = images.reshape(-1, 28, 28)
labels = np.asarray(labels).astype(int)

chosen = list("ABDEFGIJLNOPQTXZ")   # 16 letters
rows, cols = 2, 8
fig, ax = plt.subplots(rows, cols, figsize=(20, 5))
ax = ax.ravel()

for i, ch in enumerate(chosen):
    lab = ord(ch.lower()) - 96       # 'a'->1, ..., 'z'->26
    class_imgs = images[labels == lab]

    avg_img = np.mean(class_imgs, axis=0) if len(class_imgs) else np.zeros((28, 28))
    ax[i].imshow(avg_img, cmap='gray')
    ax[i].set_title(ch, fontsize=18, fontweight='bold')
    ax[i].axis('off')

# hide any leftover axes (shouldn't be any, but just in case)
for j in range(len(chosen), rows*cols):
    ax[j].axis('off')

plt.tight_layout()
plt.show()

# Normalize pixel values to [-1, 1]
images = (images - 127.5) / 127.5

# Reshape and fix orientation: rotate 90° clockwise then flip horizontally
images = images.reshape(-1, 28, 28)
images = np.array([np.fliplr(np.rot90(img, k=-1)) for img in images])  # Changed k=1 to k=-1

# Reshape back to (N, 28, 28, 1) for CNN/GAN input
images = images.reshape(-1, 28, 28, 1)

# Confirm shape
print("Final image shape:", images.shape)

Final image shape: (54586, 28, 28, 1)

# Helper function to plot images by class
def plot_class_images(class_label, n=5):
    indices = df[df.iloc[:, 0] == class_label].index[:n]
    plt.figure(figsize=(10, 2))
    for i, idx in enumerate(indices):
        img = images[idx].reshape(28, 28)
        plt.subplot(1, n, i + 1)
        plt.imshow(img, cmap='gray')
        plt.title(f"Label: {class_label}")
        plt.axis('off')
    plt.tight_layout()
    plt.show()

plot_class_images(1)
plot_class_images(2)
plot_class_images(4)
plot_class_images(5)
plot_class_images(6)

latent_dim = 150
num_classes = 16
image_shape = (28, 28, 1)

import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import UpSampling2D

latent_dim = 150
num_classes = 16
image_shape = (28, 28, 1)
batch_size = 64

# === Baseline Generator ===
def build_baseline_generator(latent_dim, channels=1):
    model = Sequential([
        Dense(7 * 7 * 128, activation='relu', input_dim=latent_dim),  # same units as CGAN
        Reshape((7, 7, 128)),

        UpSampling2D(),
        Conv2D(128, kernel_size=3, padding='same'),
        BatchNormalization(),
        Activation('relu'),

        UpSampling2D(),
        Conv2D(64, kernel_size=3, padding='same'),
        BatchNormalization(),
        Activation('relu'),

        Conv2D(channels, kernel_size=3, padding='same'),
        Activation('tanh'),
    ])

    noise = Input(shape=(latent_dim,))
    img = model(noise)
    return Model(noise, img, name="DCGAN_Generator")


# === Baseline Discriminator ===
def build_baseline_discriminator(img_shape):
    model = Sequential([
        Conv2D(64, kernel_size=3, strides=2, padding='same', input_shape=img_shape),
        LeakyReLU(alpha=0.2),
        Dropout(0.2),  # same as CGAN

        Conv2D(128, kernel_size=3, strides=2, padding='same'),
        BatchNormalization(),
        LeakyReLU(alpha=0.2),
        Dropout(0.3),  # same as CGAN

        Flatten(),
        Dense(1, activation='sigmoid'),
    ])

    img = Input(shape=img_shape)
    validity = model(img)
    return Model(img, validity, name="DCGAN_Discriminator")

## Instatantiate Models 
gen = build_baseline_generator(latent_dim, channels=1)
disc = build_baseline_discriminator(image_shape)

print("=== Generator Summary ===")
gen.summary()
print("\n=== Discriminator Summary ===")
disc.summary()

=== Generator Summary ===
Model: "DCGAN_Generator"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_2 (InputLayer)        [(None, 150)]             0         
                                                                 
 sequential_1 (Sequential)   (None, 28, 28, 1)         1169793   
                                                                 
=================================================================
Total params: 1,169,793
Trainable params: 1,169,409
Non-trainable params: 384
_________________________________________________________________

=== Discriminator Summary ===
Model: "DCGAN_Discriminator"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_3 (InputLayer)        [(None, 28, 28, 1)]       0         
                                                                 
 sequential_2 (Sequential)   (None, 1)                 81281     
                                                                 
=================================================================
Total params: 81,281
Trainable params: 81,025
Non-trainable params: 256
_________________________________________________________________

# DCGAN training class
class DCGANModel(tf.keras.Model):
    def __init__(self, generator, discriminator, latent_dim, from_logits=False):
        super().__init__()
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=from_logits)
        self.from_logits = from_logits

        # Trackers so Keras logs per-epoch averages into history.history
        self.g_loss_tracker = tf.keras.metrics.Mean(name="g_loss")
        self.d_loss_tracker = tf.keras.metrics.Mean(name="d_loss")
        self.d_acc_tracker  = tf.keras.metrics.Mean(name="d_accuracy")

    def compile(self, g_optimizer, d_optimizer):
        super().compile()
        self.g_optimizer = g_optimizer
        self.d_optimizer = d_optimizer

    @property
    def metrics(self):
        return [self.g_loss_tracker, self.d_loss_tracker, self.d_acc_tracker]

    def generator_loss(self, fake_output):
        return self.loss_fn(tf.ones_like(fake_output), fake_output)

    def discriminator_loss(self, real_output, fake_output):
        real_loss = self.loss_fn(tf.ones_like(real_output) * 0.9, real_output)  # smoothing
        fake_loss = self.loss_fn(tf.zeros_like(fake_output), fake_output)
        return (real_loss + fake_loss) / 2.0

    # Remove @tf.function until everything works
    def train_step(self, real_images):
        batch_size = tf.shape(real_images)[0]
        noise = tf.random.normal([batch_size, self.latent_dim])

        with tf.GradientTape() as disc_tape, tf.GradientTape() as gen_tape:
            fake_images = self.generator(noise, training=True)
            real_out = self.discriminator(real_images, training=True)
            fake_out = self.discriminator(fake_images, training=True)

            d_loss = self.discriminator_loss(real_out, fake_out)
            g_loss = self.generator_loss(fake_out)

        d_grads = disc_tape.gradient(d_loss, self.discriminator.trainable_variables)
        g_grads = gen_tape.gradient(g_loss, self.generator.trainable_variables)
        self.d_optimizer.apply_gradients(zip(d_grads, self.discriminator.trainable_variables))
        self.g_optimizer.apply_gradients(zip(g_grads, self.generator.trainable_variables))

        # Accuracy calc (probabilities: threshold 0.5; logits: threshold 0.0)
        thr = 0.0 if self.loss_fn.from_logits else 0.5
        real_correct = tf.cast(real_out > thr, tf.float32)
        fake_correct = tf.cast(fake_out < thr, tf.float32)
        d_acc = 0.5 * (tf.reduce_mean(real_correct) + tf.reduce_mean(fake_correct))

        # Update trackers
        self.g_loss_tracker.update_state(g_loss)
        self.d_loss_tracker.update_state(d_loss)
        self.d_acc_tracker.update_state(d_acc)

        return {"g_loss": self.g_loss_tracker.result(),
                "d_loss": self.d_loss_tracker.result(),
                "d_accuracy": self.d_acc_tracker.result()}

# Prepare datasets by class
def prepare_class_datasets(images, labels, batch_size=64, num_classes=16):
    class_datasets = {}
    for c in range(num_classes):
        class_imgs = images[labels == c]
        dataset = tf.data.Dataset.from_tensor_slices(class_imgs) \
                               .shuffle(buffer_size=10000) \
                               .batch(batch_size) \
                               .prefetch(tf.data.AUTOTUNE)
        class_datasets[c] = dataset
    return class_datasets

# Number of training epochs for each class
epochs = 50

# Dictionary to store trained DCGAN models for each class
dcgan_models = {}

# Dictionary to store training history (g_loss, d_loss, d_accuracy) per class
histories = {c: {"g_loss": [], "d_loss": [], "d_accuracy": []} for c in range(num_classes)}

# Loop through each class to train a class-specific DCGAN
for c in range(num_classes):
    print(f"\n=== Training DCGAN for class {c} ===")

    # Build a fresh Generator and Discriminator for this class
    G = build_baseline_generator(latent_dim, channels=1)  # 1 channel = grayscale
    D = build_baseline_discriminator(image_shape)         # Discriminator for 28×28×1 images

    # Wrap them into a custom DCGAN model
    # If Discriminator ends with sigmoid, set from_logits=False

    # Compile DCGAN with Adam optimizer (LR=0.0002, β1=0.5 → DCGAN best practice)
    
    dcgan = DCGANModel(G, D, latent_dim, from_logits=False)
    dcgan.compile(Adam(0.0002, 0.5), Adam(0.0002, 0.5))

    # Train for the specified number of epochs
    for epoch in range(epochs):
        # Fit for 1 epoch on the dataset of the current class
        hist = dcgan.fit(class_datasets[c], epochs=1, verbose=0)

        # Extract loss and accuracy from training history
        h = hist.history
        g = float(h["g_loss"][0])       # Generator loss
        d = float(h["d_loss"][0])       # Discriminator loss
        acc = float(h["d_accuracy"][0]) # Discriminator accuracy

        # Store metrics for plotting/analysis later
        histories[c]["g_loss"].append(g)
        histories[c]["d_loss"].append(d)
        histories[c]["d_accuracy"].append(acc)

        # Print training progress for this epoch
        print(f"[Class {c} | Epoch {epoch+1:03d}] G_loss={g:.4f}  D_loss={d:.4f}  D_acc={acc:.3f}")

        # Every 50 epochs, generate and display a sample image for this class
        if (epoch + 1) % 50 == 0:
            noise = tf.random.normal([1, latent_dim])  # Random noise input
            img = (G(noise, training=False)[0, :, :, 0] + 1.0) / 2.0  # Scale from [-1, 1] → [0, 1]
            plt.imshow(img, cmap='gray')
            plt.title(f"Class {c} - Epoch {epoch+1}")
            plt.axis('off')
            plt.show()

    # Save the trained DCGAN model for this class
    dcgan_models[c] = dcgan

=== Training DCGAN for class 0 ===
[Class 0 | Epoch 001] G_loss=1.0386  D_loss=0.6510  D_acc=0.661
[Class 0 | Epoch 002] G_loss=1.0614  D_loss=0.6742  D_acc=0.612
[Class 0 | Epoch 003] G_loss=1.1192  D_loss=0.6334  D_acc=0.654
[Class 0 | Epoch 004] G_loss=1.0739  D_loss=0.6543  D_acc=0.631
[Class 0 | Epoch 005] G_loss=1.0216  D_loss=0.6864  D_acc=0.602
[Class 0 | Epoch 006] G_loss=0.9825  D_loss=0.6879  D_acc=0.583
[Class 0 | Epoch 007] G_loss=0.9828  D_loss=0.6882  D_acc=0.584
[Class 0 | Epoch 008] G_loss=0.9745  D_loss=0.6868  D_acc=0.582
[Class 0 | Epoch 009] G_loss=0.9970  D_loss=0.6671  D_acc=0.600
[Class 0 | Epoch 010] G_loss=0.9963  D_loss=0.6616  D_acc=0.623
[Class 0 | Epoch 011] G_loss=1.0346  D_loss=0.6506  D_acc=0.633
[Class 0 | Epoch 012] G_loss=1.0415  D_loss=0.6457  D_acc=0.627
[Class 0 | Epoch 013] G_loss=1.0516  D_loss=0.6305  D_acc=0.650
[Class 0 | Epoch 014] G_loss=1.0495  D_loss=0.6338  D_acc=0.648
[Class 0 | Epoch 015] G_loss=1.0718  D_loss=0.6145  D_acc=0.667
[Class 0 | Epoch 016] G_loss=1.0539  D_loss=0.6159  D_acc=0.667
[Class 0 | Epoch 017] G_loss=1.0651  D_loss=0.6123  D_acc=0.672
[Class 0 | Epoch 018] G_loss=1.0550  D_loss=0.6115  D_acc=0.680
[Class 0 | Epoch 019] G_loss=1.0662  D_loss=0.6098  D_acc=0.677
[Class 0 | Epoch 020] G_loss=1.0566  D_loss=0.6123  D_acc=0.671
[Class 0 | Epoch 021] G_loss=1.0631  D_loss=0.6157  D_acc=0.664
[Class 0 | Epoch 022] G_loss=1.0454  D_loss=0.6173  D_acc=0.666
[Class 0 | Epoch 023] G_loss=1.0314  D_loss=0.6293  D_acc=0.643
[Class 0 | Epoch 024] G_loss=1.0464  D_loss=0.6246  D_acc=0.652
[Class 0 | Epoch 025] G_loss=1.0318  D_loss=0.6277  D_acc=0.646
[Class 0 | Epoch 026] G_loss=1.0285  D_loss=0.6281  D_acc=0.654
[Class 0 | Epoch 027] G_loss=1.0318  D_loss=0.6333  D_acc=0.644
[Class 0 | Epoch 028] G_loss=1.0311  D_loss=0.6330  D_acc=0.644
[Class 0 | Epoch 029] G_loss=1.0100  D_loss=0.6386  D_acc=0.640
[Class 0 | Epoch 030] G_loss=1.0079  D_loss=0.6426  D_acc=0.624
[Class 0 | Epoch 031] G_loss=1.0153  D_loss=0.6354  D_acc=0.642
[Class 0 | Epoch 032] G_loss=1.0112  D_loss=0.6422  D_acc=0.639
[Class 0 | Epoch 033] G_loss=1.0155  D_loss=0.6365  D_acc=0.635
[Class 0 | Epoch 034] G_loss=1.0175  D_loss=0.6454  D_acc=0.624
[Class 0 | Epoch 035] G_loss=1.0218  D_loss=0.6424  D_acc=0.636
[Class 0 | Epoch 036] G_loss=1.0000  D_loss=0.6508  D_acc=0.627
[Class 0 | Epoch 037] G_loss=1.0130  D_loss=0.6399  D_acc=0.631
[Class 0 | Epoch 038] G_loss=0.9792  D_loss=0.6529  D_acc=0.618
[Class 0 | Epoch 039] G_loss=1.0138  D_loss=0.6428  D_acc=0.632
[Class 0 | Epoch 040] G_loss=0.9771  D_loss=0.6506  D_acc=0.615
[Class 0 | Epoch 041] G_loss=0.9850  D_loss=0.6507  D_acc=0.617
[Class 0 | Epoch 042] G_loss=0.9972  D_loss=0.6471  D_acc=0.628
[Class 0 | Epoch 043] G_loss=0.9784  D_loss=0.6531  D_acc=0.613
[Class 0 | Epoch 044] G_loss=0.9923  D_loss=0.6459  D_acc=0.622
[Class 0 | Epoch 045] G_loss=0.9887  D_loss=0.6415  D_acc=0.626
[Class 0 | Epoch 046] G_loss=0.9701  D_loss=0.6517  D_acc=0.611
[Class 0 | Epoch 047] G_loss=0.9811  D_loss=0.6515  D_acc=0.624
[Class 0 | Epoch 048] G_loss=0.9881  D_loss=0.6483  D_acc=0.620
[Class 0 | Epoch 049] G_loss=0.9847  D_loss=0.6416  D_acc=0.626
[Class 0 | Epoch 050] G_loss=0.9756  D_loss=0.6500  D_acc=0.623

=== Training DCGAN for class 1 ===
[Class 1 | Epoch 001] G_loss=1.1714  D_loss=0.5639  D_acc=0.747
[Class 1 | Epoch 002] G_loss=1.1196  D_loss=0.6731  D_acc=0.639
[Class 1 | Epoch 003] G_loss=1.2650  D_loss=0.5606  D_acc=0.738
[Class 1 | Epoch 004] G_loss=1.2491  D_loss=0.5723  D_acc=0.724
[Class 1 | Epoch 005] G_loss=1.1792  D_loss=0.6015  D_acc=0.694
[Class 1 | Epoch 006] G_loss=1.1160  D_loss=0.6311  D_acc=0.658
[Class 1 | Epoch 007] G_loss=1.1056  D_loss=0.6324  D_acc=0.653
[Class 1 | Epoch 008] G_loss=1.0679  D_loss=0.6496  D_acc=0.637
[Class 1 | Epoch 009] G_loss=1.0529  D_loss=0.6458  D_acc=0.637
[Class 1 | Epoch 010] G_loss=1.0598  D_loss=0.6442  D_acc=0.640
[Class 1 | Epoch 011] G_loss=1.0444  D_loss=0.6415  D_acc=0.635
[Class 1 | Epoch 012] G_loss=1.0502  D_loss=0.6415  D_acc=0.637
[Class 1 | Epoch 013] G_loss=1.0458  D_loss=0.6352  D_acc=0.649
[Class 1 | Epoch 014] G_loss=1.0490  D_loss=0.6371  D_acc=0.647
[Class 1 | Epoch 015] G_loss=1.0466  D_loss=0.6337  D_acc=0.648
[Class 1 | Epoch 016] G_loss=1.0597  D_loss=0.6282  D_acc=0.663
[Class 1 | Epoch 017] G_loss=1.0734  D_loss=0.6218  D_acc=0.667
[Class 1 | Epoch 018] G_loss=1.0527  D_loss=0.6213  D_acc=0.660
[Class 1 | Epoch 019] G_loss=1.0685  D_loss=0.6163  D_acc=0.662
[Class 1 | Epoch 020] G_loss=1.0588  D_loss=0.6158  D_acc=0.671
[Class 1 | Epoch 021] G_loss=1.0433  D_loss=0.6236  D_acc=0.655
[Class 1 | Epoch 022] G_loss=1.0483  D_loss=0.6160  D_acc=0.673
[Class 1 | Epoch 023] G_loss=1.0520  D_loss=0.6188  D_acc=0.659
[Class 1 | Epoch 024] G_loss=1.0529  D_loss=0.6177  D_acc=0.665
[Class 1 | Epoch 025] G_loss=1.0423  D_loss=0.6167  D_acc=0.659
[Class 1 | Epoch 026] G_loss=1.0514  D_loss=0.6217  D_acc=0.662
[Class 1 | Epoch 027] G_loss=1.0260  D_loss=0.6309  D_acc=0.649
[Class 1 | Epoch 028] G_loss=1.0433  D_loss=0.6251  D_acc=0.653
[Class 1 | Epoch 029] G_loss=1.0503  D_loss=0.6242  D_acc=0.656
[Class 1 | Epoch 030] G_loss=1.0455  D_loss=0.6318  D_acc=0.645
[Class 1 | Epoch 031] G_loss=1.0346  D_loss=0.6349  D_acc=0.647
[Class 1 | Epoch 032] G_loss=1.0428  D_loss=0.6284  D_acc=0.650
[Class 1 | Epoch 033] G_loss=1.0343  D_loss=0.6330  D_acc=0.646
[Class 1 | Epoch 034] G_loss=1.0255  D_loss=0.6358  D_acc=0.646
[Class 1 | Epoch 035] G_loss=1.0288  D_loss=0.6321  D_acc=0.644
[Class 1 | Epoch 036] G_loss=1.0120  D_loss=0.6398  D_acc=0.635
[Class 1 | Epoch 037] G_loss=1.0286  D_loss=0.6359  D_acc=0.632
[Class 1 | Epoch 038] G_loss=1.0223  D_loss=0.6367  D_acc=0.636
[Class 1 | Epoch 039] G_loss=1.0173  D_loss=0.6386  D_acc=0.638
[Class 1 | Epoch 040] G_loss=1.0018  D_loss=0.6427  D_acc=0.631
[Class 1 | Epoch 041] G_loss=1.0257  D_loss=0.6349  D_acc=0.643
[Class 1 | Epoch 042] G_loss=1.0348  D_loss=0.6329  D_acc=0.640
[Class 1 | Epoch 043] G_loss=1.0326  D_loss=0.6399  D_acc=0.643
[Class 1 | Epoch 044] G_loss=1.0138  D_loss=0.6398  D_acc=0.635
[Class 1 | Epoch 045] G_loss=1.0126  D_loss=0.6421  D_acc=0.627
[Class 1 | Epoch 046] G_loss=0.9951  D_loss=0.6450  D_acc=0.629
[Class 1 | Epoch 047] G_loss=0.9878  D_loss=0.6483  D_acc=0.623
[Class 1 | Epoch 048] G_loss=0.9960  D_loss=0.6389  D_acc=0.634
[Class 1 | Epoch 049] G_loss=0.9850  D_loss=0.6513  D_acc=0.620
[Class 1 | Epoch 050] G_loss=0.9934  D_loss=0.6449  D_acc=0.618

=== Training DCGAN for class 2 ===
[Class 2 | Epoch 001] G_loss=1.1452  D_loss=0.5410  D_acc=0.772
[Class 2 | Epoch 002] G_loss=1.0939  D_loss=0.6757  D_acc=0.640
[Class 2 | Epoch 003] G_loss=1.2892  D_loss=0.5559  D_acc=0.728
[Class 2 | Epoch 004] G_loss=1.2523  D_loss=0.5750  D_acc=0.718
[Class 2 | Epoch 005] G_loss=1.1628  D_loss=0.6107  D_acc=0.676
[Class 2 | Epoch 006] G_loss=1.1098  D_loss=0.6403  D_acc=0.650
[Class 2 | Epoch 007] G_loss=1.0595  D_loss=0.6554  D_acc=0.627
[Class 2 | Epoch 008] G_loss=1.0652  D_loss=0.6442  D_acc=0.644
[Class 2 | Epoch 009] G_loss=1.0652  D_loss=0.6315  D_acc=0.653
[Class 2 | Epoch 010] G_loss=1.0765  D_loss=0.6223  D_acc=0.658
[Class 2 | Epoch 011] G_loss=1.1100  D_loss=0.6049  D_acc=0.681
[Class 2 | Epoch 012] G_loss=1.0989  D_loss=0.6078  D_acc=0.681
[Class 2 | Epoch 013] G_loss=1.1186  D_loss=0.5992  D_acc=0.684
[Class 2 | Epoch 014] G_loss=1.1415  D_loss=0.5985  D_acc=0.687
[Class 2 | Epoch 015] G_loss=1.1248  D_loss=0.5965  D_acc=0.686
[Class 2 | Epoch 016] G_loss=1.1394  D_loss=0.5905  D_acc=0.701
[Class 2 | Epoch 017] G_loss=1.1362  D_loss=0.5938  D_acc=0.691
[Class 2 | Epoch 018] G_loss=1.1411  D_loss=0.5878  D_acc=0.701
[Class 2 | Epoch 019] G_loss=1.1527  D_loss=0.5797  D_acc=0.715
[Class 2 | Epoch 020] G_loss=1.1490  D_loss=0.5835  D_acc=0.701
[Class 2 | Epoch 021] G_loss=1.1417  D_loss=0.5853  D_acc=0.702
[Class 2 | Epoch 022] G_loss=1.1524  D_loss=0.5810  D_acc=0.708
[Class 2 | Epoch 023] G_loss=1.1412  D_loss=0.5918  D_acc=0.694
[Class 2 | Epoch 024] G_loss=1.1193  D_loss=0.5981  D_acc=0.696
[Class 2 | Epoch 025] G_loss=1.1208  D_loss=0.5948  D_acc=0.695
[Class 2 | Epoch 026] G_loss=1.1011  D_loss=0.6061  D_acc=0.681
[Class 2 | Epoch 027] G_loss=1.1233  D_loss=0.5997  D_acc=0.683
[Class 2 | Epoch 028] G_loss=1.0996  D_loss=0.6114  D_acc=0.664
[Class 2 | Epoch 029] G_loss=1.1078  D_loss=0.6096  D_acc=0.675
[Class 2 | Epoch 030] G_loss=1.0962  D_loss=0.6115  D_acc=0.667
[Class 2 | Epoch 031] G_loss=1.0898  D_loss=0.6151  D_acc=0.664
[Class 2 | Epoch 032] G_loss=1.0978  D_loss=0.6166  D_acc=0.667
[Class 2 | Epoch 033] G_loss=1.0770  D_loss=0.6212  D_acc=0.659
[Class 2 | Epoch 034] G_loss=1.1017  D_loss=0.6109  D_acc=0.670
[Class 2 | Epoch 035] G_loss=1.0542  D_loss=0.6348  D_acc=0.647
[Class 2 | Epoch 036] G_loss=1.0791  D_loss=0.6209  D_acc=0.665
[Class 2 | Epoch 037] G_loss=1.0518  D_loss=0.6338  D_acc=0.642
[Class 2 | Epoch 038] G_loss=1.0714  D_loss=0.6209  D_acc=0.659
[Class 2 | Epoch 039] G_loss=1.0411  D_loss=0.6396  D_acc=0.639
[Class 2 | Epoch 040] G_loss=1.0332  D_loss=0.6346  D_acc=0.642
[Class 2 | Epoch 041] G_loss=1.0395  D_loss=0.6299  D_acc=0.642
[Class 2 | Epoch 042] G_loss=1.0351  D_loss=0.6378  D_acc=0.631
[Class 2 | Epoch 043] G_loss=1.0428  D_loss=0.6346  D_acc=0.648
[Class 2 | Epoch 044] G_loss=1.0204  D_loss=0.6332  D_acc=0.641
[Class 2 | Epoch 045] G_loss=1.0388  D_loss=0.6333  D_acc=0.644
[Class 2 | Epoch 046] G_loss=1.0217  D_loss=0.6331  D_acc=0.642
[Class 2 | Epoch 047] G_loss=1.0247  D_loss=0.6406  D_acc=0.631
[Class 2 | Epoch 048] G_loss=1.0438  D_loss=0.6282  D_acc=0.648
[Class 2 | Epoch 049] G_loss=1.0233  D_loss=0.6390  D_acc=0.642
[Class 2 | Epoch 050] G_loss=1.0274  D_loss=0.6453  D_acc=0.631

=== Training DCGAN for class 3 ===
[Class 3 | Epoch 001] G_loss=1.4764  D_loss=0.4372  D_acc=0.863
[Class 3 | Epoch 002] G_loss=1.1750  D_loss=0.6372  D_acc=0.667
[Class 3 | Epoch 003] G_loss=1.2351  D_loss=0.5801  D_acc=0.711
[Class 3 | Epoch 004] G_loss=1.1795  D_loss=0.6028  D_acc=0.689
[Class 3 | Epoch 005] G_loss=1.0970  D_loss=0.6433  D_acc=0.642
[Class 3 | Epoch 006] G_loss=1.0910  D_loss=0.6420  D_acc=0.650
[Class 3 | Epoch 007] G_loss=1.0897  D_loss=0.6321  D_acc=0.647
[Class 3 | Epoch 008] G_loss=1.0598  D_loss=0.6483  D_acc=0.634
[Class 3 | Epoch 009] G_loss=1.0667  D_loss=0.6389  D_acc=0.648
[Class 3 | Epoch 010] G_loss=1.0400  D_loss=0.6537  D_acc=0.626
[Class 3 | Epoch 011] G_loss=1.0324  D_loss=0.6483  D_acc=0.628
[Class 3 | Epoch 012] G_loss=1.0334  D_loss=0.6487  D_acc=0.630
[Class 3 | Epoch 013] G_loss=1.0515  D_loss=0.6466  D_acc=0.629
[Class 3 | Epoch 014] G_loss=1.0516  D_loss=0.6471  D_acc=0.636
[Class 3 | Epoch 015] G_loss=1.0335  D_loss=0.6447  D_acc=0.635
[Class 3 | Epoch 016] G_loss=1.0388  D_loss=0.6376  D_acc=0.649
[Class 3 | Epoch 017] G_loss=1.0273  D_loss=0.6398  D_acc=0.636
[Class 3 | Epoch 018] G_loss=1.0200  D_loss=0.6421  D_acc=0.633
[Class 3 | Epoch 019] G_loss=1.0074  D_loss=0.6451  D_acc=0.624
[Class 3 | Epoch 020] G_loss=1.0031  D_loss=0.6462  D_acc=0.629
[Class 3 | Epoch 021] G_loss=0.9891  D_loss=0.6496  D_acc=0.624
[Class 3 | Epoch 022] G_loss=0.9968  D_loss=0.6481  D_acc=0.626
[Class 3 | Epoch 023] G_loss=0.9853  D_loss=0.6568  D_acc=0.617
[Class 3 | Epoch 024] G_loss=0.9920  D_loss=0.6563  D_acc=0.619
[Class 3 | Epoch 025] G_loss=0.9802  D_loss=0.6545  D_acc=0.615
[Class 3 | Epoch 026] G_loss=0.9789  D_loss=0.6587  D_acc=0.605
[Class 3 | Epoch 027] G_loss=0.9728  D_loss=0.6588  D_acc=0.603
[Class 3 | Epoch 028] G_loss=0.9554  D_loss=0.6649  D_acc=0.601
[Class 3 | Epoch 029] G_loss=0.9660  D_loss=0.6616  D_acc=0.608
[Class 3 | Epoch 030] G_loss=0.9723  D_loss=0.6624  D_acc=0.604
[Class 3 | Epoch 031] G_loss=0.9524  D_loss=0.6672  D_acc=0.598
[Class 3 | Epoch 032] G_loss=0.9477  D_loss=0.6705  D_acc=0.594
[Class 3 | Epoch 033] G_loss=0.9491  D_loss=0.6704  D_acc=0.593
[Class 3 | Epoch 034] G_loss=0.9502  D_loss=0.6679  D_acc=0.589
[Class 3 | Epoch 035] G_loss=0.9415  D_loss=0.6679  D_acc=0.596
[Class 3 | Epoch 036] G_loss=0.9207  D_loss=0.6754  D_acc=0.585
[Class 3 | Epoch 037] G_loss=0.9426  D_loss=0.6735  D_acc=0.583
[Class 3 | Epoch 038] G_loss=0.9617  D_loss=0.6575  D_acc=0.608
[Class 3 | Epoch 039] G_loss=0.9102  D_loss=0.6883  D_acc=0.566
[Class 3 | Epoch 040] G_loss=0.9402  D_loss=0.6695  D_acc=0.589
[Class 3 | Epoch 041] G_loss=0.9324  D_loss=0.6737  D_acc=0.587
[Class 3 | Epoch 042] G_loss=0.9178  D_loss=0.6745  D_acc=0.582
[Class 3 | Epoch 043] G_loss=0.9135  D_loss=0.6789  D_acc=0.577
[Class 3 | Epoch 044] G_loss=0.9085  D_loss=0.6808  D_acc=0.568
[Class 3 | Epoch 045] G_loss=0.9155  D_loss=0.6773  D_acc=0.571
[Class 3 | Epoch 046] G_loss=0.9150  D_loss=0.6822  D_acc=0.569
[Class 3 | Epoch 047] G_loss=0.9260  D_loss=0.6761  D_acc=0.577
[Class 3 | Epoch 048] G_loss=0.9486  D_loss=0.6682  D_acc=0.586
[Class 3 | Epoch 049] G_loss=0.9173  D_loss=0.6822  D_acc=0.567
[Class 3 | Epoch 050] G_loss=0.9197  D_loss=0.6744  D_acc=0.579

=== Training DCGAN for class 4 ===
[Class 4 | Epoch 001] G_loss=1.1932  D_loss=0.5132  D_acc=0.795
[Class 4 | Epoch 002] G_loss=1.1142  D_loss=0.6665  D_acc=0.645
[Class 4 | Epoch 003] G_loss=1.2624  D_loss=0.5676  D_acc=0.720
[Class 4 | Epoch 004] G_loss=1.2702  D_loss=0.5678  D_acc=0.725
[Class 4 | Epoch 005] G_loss=1.1651  D_loss=0.6204  D_acc=0.673
[Class 4 | Epoch 006] G_loss=1.1045  D_loss=0.6427  D_acc=0.658
[Class 4 | Epoch 007] G_loss=1.0486  D_loss=0.6738  D_acc=0.615
[Class 4 | Epoch 008] G_loss=1.0121  D_loss=0.6800  D_acc=0.604
[Class 4 | Epoch 009] G_loss=1.0105  D_loss=0.6706  D_acc=0.608
[Class 4 | Epoch 010] G_loss=1.0138  D_loss=0.6573  D_acc=0.619
[Class 4 | Epoch 011] G_loss=1.0412  D_loss=0.6437  D_acc=0.636
[Class 4 | Epoch 012] G_loss=1.0498  D_loss=0.6338  D_acc=0.650
[Class 4 | Epoch 013] G_loss=1.0511  D_loss=0.6427  D_acc=0.643
[Class 4 | Epoch 014] G_loss=1.0188  D_loss=0.6533  D_acc=0.624
[Class 4 | Epoch 015] G_loss=1.0042  D_loss=0.6540  D_acc=0.632
[Class 4 | Epoch 016] G_loss=1.0353  D_loss=0.6355  D_acc=0.641
[Class 4 | Epoch 017] G_loss=1.0218  D_loss=0.6330  D_acc=0.646
[Class 4 | Epoch 018] G_loss=1.0205  D_loss=0.6366  D_acc=0.640
[Class 4 | Epoch 019] G_loss=1.0130  D_loss=0.6379  D_acc=0.645
[Class 4 | Epoch 020] G_loss=1.0082  D_loss=0.6396  D_acc=0.634
[Class 4 | Epoch 021] G_loss=1.0120  D_loss=0.6330  D_acc=0.648
[Class 4 | Epoch 022] G_loss=1.0025  D_loss=0.6409  D_acc=0.634
[Class 4 | Epoch 023] G_loss=1.0103  D_loss=0.6390  D_acc=0.634
[Class 4 | Epoch 024] G_loss=1.0107  D_loss=0.6388  D_acc=0.637
[Class 4 | Epoch 025] G_loss=1.0138  D_loss=0.6326  D_acc=0.648
[Class 4 | Epoch 026] G_loss=1.0044  D_loss=0.6329  D_acc=0.646
[Class 4 | Epoch 027] G_loss=1.0074  D_loss=0.6381  D_acc=0.631
[Class 4 | Epoch 028] G_loss=1.0101  D_loss=0.6345  D_acc=0.638
[Class 4 | Epoch 029] G_loss=0.9924  D_loss=0.6473  D_acc=0.624
[Class 4 | Epoch 030] G_loss=0.9991  D_loss=0.6374  D_acc=0.640
[Class 4 | Epoch 031] G_loss=1.0035  D_loss=0.6455  D_acc=0.630
[Class 4 | Epoch 032] G_loss=0.9819  D_loss=0.6557  D_acc=0.618
[Class 4 | Epoch 033] G_loss=0.9974  D_loss=0.6388  D_acc=0.633
[Class 4 | Epoch 034] G_loss=0.9818  D_loss=0.6496  D_acc=0.626
[Class 4 | Epoch 035] G_loss=0.9689  D_loss=0.6576  D_acc=0.606
[Class 4 | Epoch 036] G_loss=0.9817  D_loss=0.6494  D_acc=0.623
[Class 4 | Epoch 037] G_loss=0.9671  D_loss=0.6573  D_acc=0.613
[Class 4 | Epoch 038] G_loss=0.9771  D_loss=0.6560  D_acc=0.610
[Class 4 | Epoch 039] G_loss=0.9716  D_loss=0.6530  D_acc=0.620
[Class 4 | Epoch 040] G_loss=0.9756  D_loss=0.6500  D_acc=0.619
[Class 4 | Epoch 041] G_loss=0.9619  D_loss=0.6591  D_acc=0.605
[Class 4 | Epoch 042] G_loss=0.9639  D_loss=0.6543  D_acc=0.609
[Class 4 | Epoch 043] G_loss=0.9584  D_loss=0.6548  D_acc=0.614
[Class 4 | Epoch 044] G_loss=0.9504  D_loss=0.6611  D_acc=0.606
[Class 4 | Epoch 045] G_loss=0.9580  D_loss=0.6579  D_acc=0.604
[Class 4 | Epoch 046] G_loss=0.9555  D_loss=0.6560  D_acc=0.603
[Class 4 | Epoch 047] G_loss=0.9472  D_loss=0.6587  D_acc=0.602
[Class 4 | Epoch 048] G_loss=0.9494  D_loss=0.6580  D_acc=0.604
[Class 4 | Epoch 049] G_loss=0.9420  D_loss=0.6625  D_acc=0.606
[Class 4 | Epoch 050] G_loss=0.9486  D_loss=0.6629  D_acc=0.602

=== Training DCGAN for class 5 ===
[Class 5 | Epoch 001] G_loss=0.9468  D_loss=0.6881  D_acc=0.626
[Class 5 | Epoch 002] G_loss=1.0989  D_loss=0.6693  D_acc=0.626
[Class 5 | Epoch 003] G_loss=1.1837  D_loss=0.6067  D_acc=0.680
[Class 5 | Epoch 004] G_loss=1.1286  D_loss=0.6280  D_acc=0.674
[Class 5 | Epoch 005] G_loss=1.0986  D_loss=0.6413  D_acc=0.645
[Class 5 | Epoch 006] G_loss=1.0272  D_loss=0.6713  D_acc=0.617
[Class 5 | Epoch 007] G_loss=1.0069  D_loss=0.6826  D_acc=0.601
[Class 5 | Epoch 008] G_loss=1.0213  D_loss=0.6619  D_acc=0.623
[Class 5 | Epoch 009] G_loss=1.0214  D_loss=0.6548  D_acc=0.627
[Class 5 | Epoch 010] G_loss=1.0304  D_loss=0.6455  D_acc=0.630
[Class 5 | Epoch 011] G_loss=1.0478  D_loss=0.6360  D_acc=0.642
[Class 5 | Epoch 012] G_loss=1.0753  D_loss=0.6236  D_acc=0.659
[Class 5 | Epoch 013] G_loss=1.0579  D_loss=0.6295  D_acc=0.653
[Class 5 | Epoch 014] G_loss=1.0653  D_loss=0.6245  D_acc=0.653
[Class 5 | Epoch 015] G_loss=1.0698  D_loss=0.6185  D_acc=0.668
[Class 5 | Epoch 016] G_loss=1.0516  D_loss=0.6206  D_acc=0.661
[Class 5 | Epoch 017] G_loss=1.0528  D_loss=0.6169  D_acc=0.668
[Class 5 | Epoch 018] G_loss=1.0596  D_loss=0.6134  D_acc=0.668
[Class 5 | Epoch 019] G_loss=1.0639  D_loss=0.6092  D_acc=0.678
[Class 5 | Epoch 020] G_loss=1.0505  D_loss=0.6111  D_acc=0.675
[Class 5 | Epoch 021] G_loss=1.0624  D_loss=0.6111  D_acc=0.674
[Class 5 | Epoch 022] G_loss=1.0466  D_loss=0.6172  D_acc=0.663
[Class 5 | Epoch 023] G_loss=1.0506  D_loss=0.6169  D_acc=0.670
[Class 5 | Epoch 024] G_loss=1.0519  D_loss=0.6191  D_acc=0.661
[Class 5 | Epoch 025] G_loss=1.0511  D_loss=0.6198  D_acc=0.657
[Class 5 | Epoch 026] G_loss=1.0433  D_loss=0.6251  D_acc=0.655
[Class 5 | Epoch 027] G_loss=1.0398  D_loss=0.6225  D_acc=0.661
[Class 5 | Epoch 028] G_loss=1.0380  D_loss=0.6273  D_acc=0.651
[Class 5 | Epoch 029] G_loss=1.0374  D_loss=0.6300  D_acc=0.649
[Class 5 | Epoch 030] G_loss=1.0290  D_loss=0.6330  D_acc=0.643
[Class 5 | Epoch 031] G_loss=1.0267  D_loss=0.6354  D_acc=0.643
[Class 5 | Epoch 032] G_loss=1.0255  D_loss=0.6315  D_acc=0.653
[Class 5 | Epoch 033] G_loss=1.0211  D_loss=0.6408  D_acc=0.630
[Class 5 | Epoch 034] G_loss=1.0185  D_loss=0.6431  D_acc=0.627
[Class 5 | Epoch 035] G_loss=1.0252  D_loss=0.6332  D_acc=0.637
[Class 5 | Epoch 036] G_loss=1.0211  D_loss=0.6365  D_acc=0.635
[Class 5 | Epoch 037] G_loss=1.0177  D_loss=0.6362  D_acc=0.632
[Class 5 | Epoch 038] G_loss=1.0056  D_loss=0.6399  D_acc=0.635
[Class 5 | Epoch 039] G_loss=1.0106  D_loss=0.6385  D_acc=0.640
[Class 5 | Epoch 040] G_loss=0.9973  D_loss=0.6420  D_acc=0.631
[Class 5 | Epoch 041] G_loss=0.9975  D_loss=0.6414  D_acc=0.633
[Class 5 | Epoch 042] G_loss=1.0083  D_loss=0.6498  D_acc=0.621
[Class 5 | Epoch 043] G_loss=1.0063  D_loss=0.6374  D_acc=0.640
[Class 5 | Epoch 044] G_loss=1.0073  D_loss=0.6407  D_acc=0.629
[Class 5 | Epoch 045] G_loss=1.0004  D_loss=0.6359  D_acc=0.640
[Class 5 | Epoch 046] G_loss=0.9972  D_loss=0.6457  D_acc=0.631
[Class 5 | Epoch 047] G_loss=0.9737  D_loss=0.6479  D_acc=0.622
[Class 5 | Epoch 048] G_loss=1.0101  D_loss=0.6386  D_acc=0.637
[Class 5 | Epoch 049] G_loss=0.9925  D_loss=0.6458  D_acc=0.623
[Class 5 | Epoch 050] G_loss=1.0001  D_loss=0.6400  D_acc=0.634

=== Training DCGAN for class 6 ===
[Class 6 | Epoch 001] G_loss=1.3226  D_loss=0.5149  D_acc=0.795
[Class 6 | Epoch 002] G_loss=1.1253  D_loss=0.6374  D_acc=0.673
[Class 6 | Epoch 003] G_loss=1.0565  D_loss=0.6665  D_acc=0.609
[Class 6 | Epoch 004] G_loss=1.0294  D_loss=0.6741  D_acc=0.600
[Class 6 | Epoch 005] G_loss=1.0198  D_loss=0.6815  D_acc=0.598
[Class 6 | Epoch 006] G_loss=1.0092  D_loss=0.6768  D_acc=0.600
[Class 6 | Epoch 007] G_loss=1.0292  D_loss=0.6510  D_acc=0.628
[Class 6 | Epoch 008] G_loss=1.0481  D_loss=0.6434  D_acc=0.640
[Class 6 | Epoch 009] G_loss=1.0312  D_loss=0.6440  D_acc=0.637
[Class 6 | Epoch 010] G_loss=1.0655  D_loss=0.6213  D_acc=0.663
[Class 6 | Epoch 011] G_loss=1.0500  D_loss=0.6385  D_acc=0.638
[Class 6 | Epoch 012] G_loss=1.0450  D_loss=0.6270  D_acc=0.650
[Class 6 | Epoch 013] G_loss=1.0529  D_loss=0.6309  D_acc=0.652
[Class 6 | Epoch 014] G_loss=1.0277  D_loss=0.6302  D_acc=0.645
[Class 6 | Epoch 015] G_loss=1.0203  D_loss=0.6406  D_acc=0.638
[Class 6 | Epoch 016] G_loss=1.0253  D_loss=0.6339  D_acc=0.644
[Class 6 | Epoch 017] G_loss=1.0041  D_loss=0.6387  D_acc=0.633
[Class 6 | Epoch 018] G_loss=1.0264  D_loss=0.6284  D_acc=0.654
[Class 6 | Epoch 019] G_loss=1.0289  D_loss=0.6311  D_acc=0.652
[Class 6 | Epoch 020] G_loss=1.0322  D_loss=0.6403  D_acc=0.637
[Class 6 | Epoch 021] G_loss=1.0269  D_loss=0.6369  D_acc=0.643
[Class 6 | Epoch 022] G_loss=0.9975  D_loss=0.6451  D_acc=0.637
[Class 6 | Epoch 023] G_loss=0.9986  D_loss=0.6420  D_acc=0.630
[Class 6 | Epoch 024] G_loss=1.0057  D_loss=0.6474  D_acc=0.629
[Class 6 | Epoch 025] G_loss=0.9847  D_loss=0.6456  D_acc=0.626
[Class 6 | Epoch 026] G_loss=0.9976  D_loss=0.6436  D_acc=0.629
[Class 6 | Epoch 027] G_loss=0.9708  D_loss=0.6616  D_acc=0.610
[Class 6 | Epoch 028] G_loss=0.9862  D_loss=0.6572  D_acc=0.610
[Class 6 | Epoch 029] G_loss=0.9704  D_loss=0.6511  D_acc=0.609
[Class 6 | Epoch 030] G_loss=0.9782  D_loss=0.6588  D_acc=0.611
[Class 6 | Epoch 031] G_loss=0.9672  D_loss=0.6576  D_acc=0.607
[Class 6 | Epoch 032] G_loss=0.9470  D_loss=0.6679  D_acc=0.596
[Class 6 | Epoch 033] G_loss=0.9403  D_loss=0.6702  D_acc=0.596
[Class 6 | Epoch 034] G_loss=0.9500  D_loss=0.6631  D_acc=0.598
[Class 6 | Epoch 035] G_loss=0.9454  D_loss=0.6656  D_acc=0.603
[Class 6 | Epoch 036] G_loss=0.9217  D_loss=0.6748  D_acc=0.583
[Class 6 | Epoch 037] G_loss=0.9273  D_loss=0.6666  D_acc=0.593
[Class 6 | Epoch 038] G_loss=0.9355  D_loss=0.6669  D_acc=0.588
[Class 6 | Epoch 039] G_loss=0.9413  D_loss=0.6596  D_acc=0.598
[Class 6 | Epoch 040] G_loss=0.9305  D_loss=0.6647  D_acc=0.597
[Class 6 | Epoch 041] G_loss=0.9224  D_loss=0.6700  D_acc=0.581
[Class 6 | Epoch 042] G_loss=0.9379  D_loss=0.6622  D_acc=0.603
[Class 6 | Epoch 043] G_loss=0.9467  D_loss=0.6596  D_acc=0.599
[Class 6 | Epoch 044] G_loss=0.9157  D_loss=0.6766  D_acc=0.572
[Class 6 | Epoch 045] G_loss=0.9233  D_loss=0.6730  D_acc=0.584
[Class 6 | Epoch 046] G_loss=0.9570  D_loss=0.6567  D_acc=0.603
[Class 6 | Epoch 047] G_loss=0.9384  D_loss=0.6583  D_acc=0.604
[Class 6 | Epoch 048] G_loss=0.9268  D_loss=0.6695  D_acc=0.580
[Class 6 | Epoch 049] G_loss=0.9433  D_loss=0.6590  D_acc=0.602
[Class 6 | Epoch 050] G_loss=0.9145  D_loss=0.6737  D_acc=0.570

=== Training DCGAN for class 7 ===
[Class 7 | Epoch 001] G_loss=1.4097  D_loss=0.4615  D_acc=0.836
[Class 7 | Epoch 002] G_loss=1.1377  D_loss=0.6567  D_acc=0.650
[Class 7 | Epoch 003] G_loss=1.3615  D_loss=0.5432  D_acc=0.746
[Class 7 | Epoch 004] G_loss=1.3166  D_loss=0.5554  D_acc=0.741
[Class 7 | Epoch 005] G_loss=1.2213  D_loss=0.6104  D_acc=0.689
[Class 7 | Epoch 006] G_loss=1.1257  D_loss=0.6465  D_acc=0.649
[Class 7 | Epoch 007] G_loss=1.0681  D_loss=0.6668  D_acc=0.622
[Class 7 | Epoch 008] G_loss=1.0197  D_loss=0.6686  D_acc=0.614
[Class 7 | Epoch 009] G_loss=1.0539  D_loss=0.6460  D_acc=0.633
[Class 7 | Epoch 010] G_loss=1.0456  D_loss=0.6362  D_acc=0.650
[Class 7 | Epoch 011] G_loss=1.0511  D_loss=0.6340  D_acc=0.650
[Class 7 | Epoch 012] G_loss=1.0568  D_loss=0.6243  D_acc=0.659
[Class 7 | Epoch 013] G_loss=1.0822  D_loss=0.6186  D_acc=0.667
[Class 7 | Epoch 014] G_loss=1.0869  D_loss=0.6111  D_acc=0.679
[Class 7 | Epoch 015] G_loss=1.1087  D_loss=0.6066  D_acc=0.682
[Class 7 | Epoch 016] G_loss=1.0994  D_loss=0.6068  D_acc=0.687
[Class 7 | Epoch 017] G_loss=1.1230  D_loss=0.5987  D_acc=0.689
[Class 7 | Epoch 018] G_loss=1.1053  D_loss=0.5997  D_acc=0.685
[Class 7 | Epoch 019] G_loss=1.0982  D_loss=0.6065  D_acc=0.675
[Class 7 | Epoch 020] G_loss=1.0831  D_loss=0.6144  D_acc=0.673
[Class 7 | Epoch 021] G_loss=1.0920  D_loss=0.6044  D_acc=0.682
[Class 7 | Epoch 022] G_loss=1.0678  D_loss=0.6206  D_acc=0.663
[Class 7 | Epoch 023] G_loss=1.0781  D_loss=0.6143  D_acc=0.667
[Class 7 | Epoch 024] G_loss=1.0559  D_loss=0.6245  D_acc=0.658
[Class 7 | Epoch 025] G_loss=1.0621  D_loss=0.6262  D_acc=0.657
[Class 7 | Epoch 026] G_loss=1.0406  D_loss=0.6421  D_acc=0.637
[Class 7 | Epoch 027] G_loss=1.0291  D_loss=0.6372  D_acc=0.647
[Class 7 | Epoch 028] G_loss=1.0185  D_loss=0.6507  D_acc=0.618
[Class 7 | Epoch 029] G_loss=1.0074  D_loss=0.6465  D_acc=0.624
[Class 7 | Epoch 030] G_loss=1.0129  D_loss=0.6444  D_acc=0.627
[Class 7 | Epoch 031] G_loss=1.0017  D_loss=0.6526  D_acc=0.624
[Class 7 | Epoch 032] G_loss=1.0003  D_loss=0.6490  D_acc=0.617
[Class 7 | Epoch 033] G_loss=0.9952  D_loss=0.6517  D_acc=0.618
[Class 7 | Epoch 034] G_loss=0.9996  D_loss=0.6550  D_acc=0.615
[Class 7 | Epoch 035] G_loss=0.9918  D_loss=0.6580  D_acc=0.607
[Class 7 | Epoch 036] G_loss=0.9874  D_loss=0.6511  D_acc=0.618
[Class 7 | Epoch 037] G_loss=0.9861  D_loss=0.6664  D_acc=0.599
[Class 7 | Epoch 038] G_loss=0.9596  D_loss=0.6619  D_acc=0.604
[Class 7 | Epoch 039] G_loss=0.9797  D_loss=0.6596  D_acc=0.613
[Class 7 | Epoch 040] G_loss=0.9634  D_loss=0.6608  D_acc=0.602
[Class 7 | Epoch 041] G_loss=0.9676  D_loss=0.6593  D_acc=0.601
[Class 7 | Epoch 042] G_loss=0.9671  D_loss=0.6633  D_acc=0.602
[Class 7 | Epoch 043] G_loss=0.9851  D_loss=0.6559  D_acc=0.612
[Class 7 | Epoch 044] G_loss=0.9630  D_loss=0.6618  D_acc=0.603
[Class 7 | Epoch 045] G_loss=0.9636  D_loss=0.6624  D_acc=0.601
[Class 7 | Epoch 046] G_loss=0.9520  D_loss=0.6705  D_acc=0.591
[Class 7 | Epoch 047] G_loss=0.9743  D_loss=0.6596  D_acc=0.607
[Class 7 | Epoch 048] G_loss=0.9410  D_loss=0.6664  D_acc=0.594
[Class 7 | Epoch 049] G_loss=0.9475  D_loss=0.6652  D_acc=0.597
[Class 7 | Epoch 050] G_loss=0.9689  D_loss=0.6537  D_acc=0.610

=== Training DCGAN for class 8 ===
[Class 8 | Epoch 001] G_loss=1.0410  D_loss=0.6483  D_acc=0.662
[Class 8 | Epoch 002] G_loss=1.0623  D_loss=0.6983  D_acc=0.608
[Class 8 | Epoch 003] G_loss=1.1522  D_loss=0.6166  D_acc=0.675
[Class 8 | Epoch 004] G_loss=1.1292  D_loss=0.6546  D_acc=0.634
[Class 8 | Epoch 005] G_loss=1.0804  D_loss=0.6589  D_acc=0.633
[Class 8 | Epoch 006] G_loss=1.0794  D_loss=0.6437  D_acc=0.636
[Class 8 | Epoch 007] G_loss=1.0794  D_loss=0.6375  D_acc=0.646
[Class 8 | Epoch 008] G_loss=1.0876  D_loss=0.6246  D_acc=0.658
[Class 8 | Epoch 009] G_loss=1.1001  D_loss=0.6259  D_acc=0.662
[Class 8 | Epoch 010] G_loss=1.1071  D_loss=0.6175  D_acc=0.669
[Class 8 | Epoch 011] G_loss=1.0892  D_loss=0.6191  D_acc=0.674
[Class 8 | Epoch 012] G_loss=1.0784  D_loss=0.6238  D_acc=0.659
[Class 8 | Epoch 013] G_loss=1.0646  D_loss=0.6239  D_acc=0.660
[Class 8 | Epoch 014] G_loss=1.0572  D_loss=0.6259  D_acc=0.655
[Class 8 | Epoch 015] G_loss=1.0629  D_loss=0.6194  D_acc=0.661
[Class 8 | Epoch 016] G_loss=1.0661  D_loss=0.6243  D_acc=0.654
[Class 8 | Epoch 017] G_loss=1.0538  D_loss=0.6217  D_acc=0.662
[Class 8 | Epoch 018] G_loss=1.0534  D_loss=0.6231  D_acc=0.652
[Class 8 | Epoch 019] G_loss=1.0406  D_loss=0.6314  D_acc=0.651
[Class 8 | Epoch 020] G_loss=1.0290  D_loss=0.6358  D_acc=0.640
[Class 8 | Epoch 021] G_loss=1.0208  D_loss=0.6413  D_acc=0.638
[Class 8 | Epoch 022] G_loss=1.0090  D_loss=0.6486  D_acc=0.620
[Class 8 | Epoch 023] G_loss=1.0066  D_loss=0.6481  D_acc=0.625
[Class 8 | Epoch 024] G_loss=1.0043  D_loss=0.6496  D_acc=0.623
[Class 8 | Epoch 025] G_loss=0.9780  D_loss=0.6616  D_acc=0.610
[Class 8 | Epoch 026] G_loss=0.9803  D_loss=0.6548  D_acc=0.609
[Class 8 | Epoch 027] G_loss=0.9933  D_loss=0.6508  D_acc=0.618
[Class 8 | Epoch 028] G_loss=0.9725  D_loss=0.6603  D_acc=0.600
[Class 8 | Epoch 029] G_loss=0.9642  D_loss=0.6673  D_acc=0.594
[Class 8 | Epoch 030] G_loss=0.9743  D_loss=0.6601  D_acc=0.598
[Class 8 | Epoch 031] G_loss=0.9431  D_loss=0.6706  D_acc=0.587
[Class 8 | Epoch 032] G_loss=0.9578  D_loss=0.6663  D_acc=0.597
[Class 8 | Epoch 033] G_loss=0.9449  D_loss=0.6669  D_acc=0.587
[Class 8 | Epoch 034] G_loss=0.9424  D_loss=0.6712  D_acc=0.582
[Class 8 | Epoch 035] G_loss=0.9233  D_loss=0.6781  D_acc=0.572
[Class 8 | Epoch 036] G_loss=0.9225  D_loss=0.6757  D_acc=0.572
[Class 8 | Epoch 037] G_loss=0.9247  D_loss=0.6696  D_acc=0.580
[Class 8 | Epoch 038] G_loss=0.9175  D_loss=0.6769  D_acc=0.570
[Class 8 | Epoch 039] G_loss=0.9287  D_loss=0.6708  D_acc=0.591
[Class 8 | Epoch 040] G_loss=0.9234  D_loss=0.6731  D_acc=0.581
[Class 8 | Epoch 041] G_loss=0.9127  D_loss=0.6757  D_acc=0.567
[Class 8 | Epoch 042] G_loss=0.9084  D_loss=0.6818  D_acc=0.557
[Class 8 | Epoch 043] G_loss=0.9123  D_loss=0.6739  D_acc=0.578
[Class 8 | Epoch 044] G_loss=0.9068  D_loss=0.6773  D_acc=0.567
[Class 8 | Epoch 045] G_loss=0.9045  D_loss=0.6814  D_acc=0.567
[Class 8 | Epoch 046] G_loss=0.9048  D_loss=0.6777  D_acc=0.563
[Class 8 | Epoch 047] G_loss=0.9020  D_loss=0.6767  D_acc=0.571
[Class 8 | Epoch 048] G_loss=0.9059  D_loss=0.6733  D_acc=0.570
[Class 8 | Epoch 049] G_loss=0.9050  D_loss=0.6763  D_acc=0.570
[Class 8 | Epoch 050] G_loss=0.9132  D_loss=0.6732  D_acc=0.572

=== Training DCGAN for class 9 ===
[Class 9 | Epoch 001] G_loss=1.0636  D_loss=0.6391  D_acc=0.681
[Class 9 | Epoch 002] G_loss=1.1021  D_loss=0.6654  D_acc=0.633
[Class 9 | Epoch 003] G_loss=1.1259  D_loss=0.6376  D_acc=0.643
[Class 9 | Epoch 004] G_loss=1.0588  D_loss=0.6671  D_acc=0.609
[Class 9 | Epoch 005] G_loss=1.0020  D_loss=0.6955  D_acc=0.584
[Class 9 | Epoch 006] G_loss=0.9932  D_loss=0.6872  D_acc=0.593
[Class 9 | Epoch 007] G_loss=1.0040  D_loss=0.6764  D_acc=0.596
[Class 9 | Epoch 008] G_loss=1.0052  D_loss=0.6678  D_acc=0.610
[Class 9 | Epoch 009] G_loss=1.0099  D_loss=0.6643  D_acc=0.612
[Class 9 | Epoch 010] G_loss=1.0166  D_loss=0.6543  D_acc=0.623
[Class 9 | Epoch 011] G_loss=1.0273  D_loss=0.6463  D_acc=0.629
[Class 9 | Epoch 012] G_loss=1.0179  D_loss=0.6441  D_acc=0.634
[Class 9 | Epoch 013] G_loss=1.0415  D_loss=0.6313  D_acc=0.652
[Class 9 | Epoch 014] G_loss=1.0607  D_loss=0.6263  D_acc=0.651
[Class 9 | Epoch 015] G_loss=1.0552  D_loss=0.6301  D_acc=0.649
[Class 9 | Epoch 016] G_loss=1.0337  D_loss=0.6322  D_acc=0.652
[Class 9 | Epoch 017] G_loss=1.0291  D_loss=0.6364  D_acc=0.639
[Class 9 | Epoch 018] G_loss=1.0404  D_loss=0.6301  D_acc=0.638
[Class 9 | Epoch 019] G_loss=1.0312  D_loss=0.6312  D_acc=0.649
[Class 9 | Epoch 020] G_loss=1.0282  D_loss=0.6289  D_acc=0.645
[Class 9 | Epoch 021] G_loss=1.0318  D_loss=0.6337  D_acc=0.647
[Class 9 | Epoch 022] G_loss=1.0065  D_loss=0.6394  D_acc=0.638
[Class 9 | Epoch 023] G_loss=1.0172  D_loss=0.6353  D_acc=0.642
[Class 9 | Epoch 024] G_loss=1.0131  D_loss=0.6487  D_acc=0.624
[Class 9 | Epoch 025] G_loss=1.0276  D_loss=0.6361  D_acc=0.640
[Class 9 | Epoch 026] G_loss=0.9976  D_loss=0.6429  D_acc=0.625
[Class 9 | Epoch 027] G_loss=1.0218  D_loss=0.6369  D_acc=0.627
[Class 9 | Epoch 028] G_loss=1.0006  D_loss=0.6436  D_acc=0.629
[Class 9 | Epoch 029] G_loss=1.0191  D_loss=0.6322  D_acc=0.643
[Class 9 | Epoch 030] G_loss=0.9982  D_loss=0.6447  D_acc=0.631
[Class 9 | Epoch 031] G_loss=1.0071  D_loss=0.6361  D_acc=0.633
[Class 9 | Epoch 032] G_loss=1.0098  D_loss=0.6385  D_acc=0.636
[Class 9 | Epoch 033] G_loss=0.9898  D_loss=0.6457  D_acc=0.631
[Class 9 | Epoch 034] G_loss=1.0157  D_loss=0.6378  D_acc=0.627
[Class 9 | Epoch 035] G_loss=1.0010  D_loss=0.6386  D_acc=0.632
[Class 9 | Epoch 036] G_loss=1.0015  D_loss=0.6487  D_acc=0.625
[Class 9 | Epoch 037] G_loss=0.9880  D_loss=0.6454  D_acc=0.627
[Class 9 | Epoch 038] G_loss=1.0013  D_loss=0.6403  D_acc=0.631
[Class 9 | Epoch 039] G_loss=0.9966  D_loss=0.6433  D_acc=0.629
[Class 9 | Epoch 040] G_loss=0.9884  D_loss=0.6463  D_acc=0.624
[Class 9 | Epoch 041] G_loss=0.9862  D_loss=0.6443  D_acc=0.625
[Class 9 | Epoch 042] G_loss=0.9985  D_loss=0.6390  D_acc=0.630
[Class 9 | Epoch 043] G_loss=0.9905  D_loss=0.6470  D_acc=0.625
[Class 9 | Epoch 044] G_loss=0.9928  D_loss=0.6414  D_acc=0.632
[Class 9 | Epoch 045] G_loss=0.9874  D_loss=0.6458  D_acc=0.620
[Class 9 | Epoch 046] G_loss=0.9800  D_loss=0.6501  D_acc=0.626
[Class 9 | Epoch 047] G_loss=0.9880  D_loss=0.6423  D_acc=0.625
[Class 9 | Epoch 048] G_loss=0.9827  D_loss=0.6419  D_acc=0.629
[Class 9 | Epoch 049] G_loss=0.9852  D_loss=0.6468  D_acc=0.628
[Class 9 | Epoch 050] G_loss=0.9781  D_loss=0.6445  D_acc=0.625

def plot_all_classes(histories):
    num_classes = len(histories)
    fig, axes = plt.subplots(num_classes, 2, figsize=(12, 4*num_classes))

    for idx, c in enumerate(histories.keys()):
        e = range(1, len(histories[c]["g_loss"]) + 1)

        # Loss subplot
        axes[idx, 0].plot(e, histories[c]["g_loss"], label="G Loss")
        axes[idx, 0].plot(e, histories[c]["d_loss"], label="D Loss")
        axes[idx, 0].set_title(f"Class {c}: Loss")
        axes[idx, 0].set_xlabel("Epoch")
        axes[idx, 0].set_ylabel("Loss")
        axes[idx, 0].legend()
        axes[idx, 0].grid(True)

        # Accuracy subplot
        axes[idx, 1].plot(e, histories[c]["d_accuracy"], label="D Accuracy")
        axes[idx, 1].set_title(f"Class {c}: Accuracy")
        axes[idx, 1].set_xlabel("Epoch")
        axes[idx, 1].set_ylabel("Accuracy")
        axes[idx, 1].legend()
        axes[idx, 1].grid(True)

    plt.tight_layout()
    plt.show()
plot_all_classes(histories)

def plot_overall_accuracy(histories):
    num_classes = len(histories)
    num_epochs = len(next(iter(histories.values()))["d_accuracy"])
    # average across classes per epoch
    avg_acc = [
        sum(histories[c]["d_accuracy"][ep] for c in histories) / num_classes
        for ep in range(num_epochs)
    ]
    plt.figure(figsize=(6,4))
    plt.plot(range(1, num_epochs+1), avg_acc, label="Avg D Accuracy (all classes)")
    plt.xlabel("Epoch"); plt.ylabel("Accuracy"); plt.title("Overall Discriminator Accuracy")
    plt.ylim(0,1); plt.legend(); plt.grid(True); plt.show()

# Example:
plot_overall_accuracy(histories)

import matplotlib.pyplot as plt
import numpy as np
import os
import tensorflow as tf

def generate_classwise_grid(dcgan_models, latent_dim, save_dir="DCGAN_16Models/Baseline"):
    rows = 16  # number of classes
    cols = 10  # number of samples per class

    os.makedirs(save_dir, exist_ok=True)

    fig, axes = plt.subplots(rows, cols, figsize=(cols, rows))
    fig.subplots_adjust(hspace=0.1, wspace=0.1)

    for class_id in range(16):
        generator = dcgan_models[class_id].generator

        noise = tf.random.normal([cols, latent_dim])
        labels = tf.constant([[class_id]] * cols, dtype=tf.int32)
        generated_imgs = generator([noise, labels], training=False)

        generated_imgs = (generated_imgs + 1.0) / 2.0  # scale from [-1,1] to [0,1]

        for j in range(cols):
            ax = axes[class_id, j]
            ax.imshow(generated_imgs[j, :, :, 0], cmap='gray')
            ax.axis('off')

    save_path = os.path.join(save_dir, "dcgan_16x10_grid.png")
    plt.savefig(save_path, bbox_inches='tight', pad_inches=0.1)
    plt.close()
    print(f"Saved 160-image grid to: {save_path}")

label_to_char = {
    0: 'A', 1: 'B', 2: 'D', 3: 'E',
    4: 'F', 5: 'G', 6: 'I', 7: 'J',
    8: 'L', 9: 'N', 10: 'O', 11: 'P',
    12: 'Q', 13: 'T', 14: 'X', 15: 'Z'
}

def generate_word(dcgan_models, word, latent_dim, label_to_char):
    # Reverse the label_to_char mapping
    char_to_label = {v: k for k, v in label_to_char.items()}

    fig, axs = plt.subplots(1, len(word), figsize=(len(word) * 1.5, 2))

    for i, char in enumerate(word.upper()):
        label = char_to_label.get(char, None)
        if label is None:
            print(f"Character '{char}' not found in label_to_char mapping. Skipping.")
            continue

        # Get the corresponding generator
        generator = dcgan_models[label].generator

        # Generate a noise vector and produce image
        noise = tf.random.normal([1, latent_dim])
        gen_img = generator(noise, training=False)
        img = (gen_img[0, :, :, 0] + 1.0) / 2.0  # Rescale from [-1, 1] to [0, 1]

        # Plot
        axs[i].imshow(img, cmap='gray')
        axs[i].axis('off')
        axs[i].set_title(char)

    plt.tight_layout()
    plt.show()

generate_word(dcgan_models, "DAAA", latent_dim, label_to_char)
generate_word(dcgan_models, "DEEP LEANING", latent_dim, label_to_char)

Character ' ' not found in label_to_char mapping. Skipping.

import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.layers import (Input, Dense, Reshape, Conv2DTranspose, BatchNormalization,
                                     Activation, Conv2D, LeakyReLU, Dropout, Flatten)
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.optimizers import Adam

# === Generator ===
def build_generator(latent_dim, channels=1):
    model = Sequential([
        Dense(256 * 7 * 7, input_dim=latent_dim),  # More neurons here
        BatchNormalization(),
        LeakyReLU(0.2),
        Reshape((7, 7, 256)),

        # Add more Conv2DTranspose blocks
        Conv2DTranspose(128, kernel_size=4, strides=2, padding='same'),  # → 14x14
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2DTranspose(64, kernel_size=4, strides=2, padding='same'),   # → 28x28
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2DTranspose(32, kernel_size=3, strides=1, padding='same'),   # refinement layer
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2D(channels, kernel_size=3, padding='same'),
        Activation('tanh')
    ])

    noise = Input(shape=(latent_dim,))
    img = model(noise)
    return Model(noise, img)

# === Discriminator ===
def build_discriminator(img_shape):
    model = Sequential([
        # Block 1
        Conv2D(64, kernel_size=3, strides=2, padding='same', input_shape=img_shape),
        LeakyReLU(0.2),
        Dropout(0.3),

        # Block 2
        Conv2D(128, kernel_size=3, strides=2, padding='same'),
        BatchNormalization(),
        LeakyReLU(0.2),
        Dropout(0.3),

        # Block 3
        Conv2D(128, kernel_size=3, strides=1, padding='same'),
        BatchNormalization(),
        LeakyReLU(0.2),

        Flatten(),
        Dense(64),
        LeakyReLU(0.2),
        Dense(1, activation='sigmoid')
    ])

    img = Input(shape=img_shape)
    validity = model(img)
    return Model(img, validity)

latent_dim = 128
img_shape = (28, 28, 1)
batch_size = 128
epochs = 500
num_classes = 16

class_datasets = prepare_class_datasets(images, mapped_labels, batch_size=batch_size, num_classes=num_classes)

# Build models
generator = build_generator(latent_dim, channels=1)
discriminator = build_discriminator(img_shape)

# Print summaries
print("=== Generator Summary ===")
generator.summary()

print("\n=== Discriminator Summary ===")
discriminator.summary()

=== Generator Summary ===
Model: "model_178"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_147 (InputLayer)      [(None, 128)]             0         
                                                                 
 sequential_146 (Sequential)  (None, 28, 28, 1)        2343553   
                                                                 
=================================================================
Total params: 2,343,553
Trainable params: 2,318,017
Non-trainable params: 25,536
_________________________________________________________________

=== Discriminator Summary ===
Model: "model_179"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_148 (InputLayer)      [(None, 28, 28, 1)]       0         
                                                                 
 sequential_147 (Sequential)  (None, 1)                624641    
                                                                 
=================================================================
Total params: 624,641
Trainable params: 624,129
Non-trainable params: 512
_________________________________________________________________

# === DCGAN Model Class ===
class DCGANModel(tf.keras.Model):
    def __init__(self, generator, discriminator, latent_dim):
        super().__init__()
        self.generator = generator          # Generator network
        self.discriminator = discriminator  # Discriminator network
        self.latent_dim = latent_dim         # Size of random noise vector (latent space)
        self.loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=False)  # Loss function for binary classification

    def compile(self, g_optimizer, d_optimizer):
        super().compile()
        self.g_optimizer = g_optimizer  # Optimizer for the generator
        self.d_optimizer = d_optimizer  # Optimizer for the discriminator

    # Generator loss: tries to make fake images look real (labels = 1)
    def generator_loss(self, fake_output):
        return self.loss_fn(tf.ones_like(fake_output), fake_output)

    # Discriminator loss: real images → label 1, fake images → label 0
    def discriminator_loss(self, real_output, fake_output):
        real_loss = self.loss_fn(tf.ones_like(real_output), real_output)  
        fake_loss = self.loss_fn(tf.zeros_like(fake_output), fake_output)
        return real_loss + fake_loss

    @tf.function
    def train_step(self, real_images):
        # 1. Get batch size and generate random noise
        batch_size = tf.shape(real_images)[0]
        noise = tf.random.normal([batch_size, self.latent_dim])

        # 2. Record gradients for both networks
        with tf.GradientTape() as disc_tape, tf.GradientTape() as gen_tape:
            # Generate fake images from noise
            generated_images = self.generator(noise, training=True)
            # Discriminator predictions for real and fake images
            real_output = self.discriminator(real_images, training=True)
            fake_output = self.discriminator(generated_images, training=True)
            # Calculate losses
            d_loss = self.discriminator_loss(real_output, fake_output)
            g_loss = self.generator_loss(fake_output)

        # 3. Compute and apply gradients for both networks
        grads_d = disc_tape.gradient(d_loss, self.discriminator.trainable_variables)
        grads_g = gen_tape.gradient(g_loss, self.generator.trainable_variables)
        self.d_optimizer.apply_gradients(zip(grads_d, self.discriminator.trainable_variables))
        self.g_optimizer.apply_gradients(zip(grads_g, self.generator.trainable_variables))

        # 4. Calculate discriminator accuracy:
        #    Real images → correctly classified as real (> 0.5)
        #    Fake images → correctly classified as fake (< 0.5)
        real_preds = tf.cast(real_output > 0.5, tf.float32)
        fake_preds = tf.cast(fake_output < 0.5, tf.float32)
        real_accuracy = tf.reduce_mean(real_preds)
        fake_accuracy = tf.reduce_mean(fake_preds)
        d_accuracy = 0.5 * (real_accuracy + fake_accuracy)

        # 5. Return training metrics
        return {
            "d_loss": d_loss,       # Discriminator loss
            "g_loss": g_loss,       # Generator loss
            "d_accuracy": d_accuracy # Discriminator accuracy
        }


# === Callback to Save Best Generator ===
class SaveBestGenerator(tf.keras.callbacks.Callback):
    def __init__(self, save_path):
        super().__init__()
        self.save_path = save_path
        self.best_loss = float('inf')  # Track best generator loss

    def on_epoch_end(self, epoch, logs=None):
        # Save generator weights if loss improved
        g_loss = logs.get('g_loss')
        if g_loss is not None and g_loss < self.best_loss:
            print(f"\nEpoch {epoch + 1}: Generator loss improved from {self.best_loss:.4f} to {g_loss:.4f}. Saving weights.")
            self.best_loss = g_loss
            self.model.generator.save_weights(self.save_path)


# === Callback to Print Images Every N Epochs ===
class PrintImagesEveryN(tf.keras.callbacks.Callback):
    def __init__(self, class_index, latent_dim, interval=250):
        super().__init__()
        self.class_index = class_index
        self.latent_dim = latent_dim
        self.interval = interval
        # Fixed noise seed to see consistent sample evolution over time
        self.seed = tf.random.normal([10, latent_dim])

    def on_epoch_end(self, epoch, logs=None):
        # Every N epochs, generate and show sample images
        if (epoch + 1) % self.interval == 0:
            generated_images = self.model.generator(self.seed, training=False)
            generated_images = 0.5 * generated_images + 0.5  # Rescale from [-1, 1] to [0, 1]
            print(f"\nClass {self.class_index} — Epoch {epoch + 1} preview:")
            fig, axes = plt.subplots(1, 10, figsize=(20, 2))
            for i in range(10):
                ax = axes[i]
                ax.imshow(generated_images[i, :, :, 0], cmap='gray')
                ax.axis('off')
            plt.show()


# === Prepare Dataset Per Class ===
def prepare_class_datasets(images, labels, batch_size=128, num_classes=16):
    """
    Creates a dictionary where each key is a class index and value is a tf.data.Dataset
    containing only images of that class.
    """
    class_datasets = {}
    for c in range(num_classes):
        class_imgs = images[labels == c]
        dataset = tf.data.Dataset.from_tensor_slices(class_imgs) \
                                 .shuffle(buffer_size=10000) \
                                 .batch(batch_size) \
                                 .prefetch(tf.data.AUTOTUNE)
        class_datasets[c] = dataset
    return class_datasets


# === Callback to Print Discriminator Accuracy ===
class PrintAccuracy(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        # Display discriminator accuracy at the end of each epoch if available
        if logs and 'd_accuracy' in logs:
            print(f"Epoch {epoch + 1}: D Accuracy = {logs['d_accuracy']:.4f}")

# === Training ===

dcgan_models = {}
histories = {}

# Directory to save best weights
best_save_dir = "16Models_saved_generators_600epochs"
os.makedirs(best_save_dir, exist_ok=True)

for c in range(num_classes):
    print(f"Training DCGAN for class {c}...")

    generator = build_generator(latent_dim, channels=1)
    discriminator = build_discriminator(img_shape)

    dcgan = DCGANModel(generator, discriminator, latent_dim)
    dcgan.compile(
        g_optimizer=Adam(0.0002, beta_1=0.5),
        d_optimizer=Adam(0.0002, beta_1=0.5)
    )

    preview_callback = PrintImagesEveryN(class_index=c, latent_dim=latent_dim, interval=100)
    best_weight_path = os.path.join(best_save_dir, f"best_generator_class_{c}.h5")
    save_best_callback = SaveBestGenerator(save_path=best_weight_path)
    accuracy_callback = PrintAccuracy()  

    history = dcgan.fit(
        class_datasets[c],
        epochs=epochs,
        callbacks=[preview_callback, save_best_callback, accuracy_callback]
    )

    dcgan_models[c] = dcgan
    histories[c] = history

# === Save final generator weights for all 16 classes ===
final_save_dir = "16Models_saved_generators_600epochs(last)"
os.makedirs(final_save_dir, exist_ok=True)

for class_idx, dcgan in dcgan_models.items():
    gen = dcgan.generator
    weight_path = os.path.join(final_save_dir, f"final_generator_class_{class_idx}.h5")
    gen.save_weights(weight_path)
    print(f"Saved final generator weights for class {class_idx} to {weight_path}")

Training DCGAN for class 0...
Epoch 1/500
27/27 [==============================] - ETA: 0s - d_loss: 0.6930 - g_loss: 2.3278 - d_accuracy: 0.9089
Epoch 1: Generator loss improved from inf to 3.3849. Saving weights.
Epoch 1: D Accuracy = 0.8750
27/27 [==============================] - 4s 40ms/step - d_loss: 0.6916 - g_loss: 2.3656 - d_accuracy: 0.9076
Epoch 2/500
27/27 [==============================] - ETA: 0s - d_loss: 0.5277 - g_loss: 3.7832 - d_accuracy: 0.9584Epoch 2: D Accuracy = 0.9926
27/27 [==============================] - 1s 32ms/step - d_loss: 0.5239 - g_loss: 3.7931 - d_accuracy: 0.9596
Epoch 3/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9416 - g_loss: 2.7182 - d_accuracy: 0.8639
Epoch 3: Generator loss improved from 3.3849 to 1.4738. Saving weights.
Epoch 3: D Accuracy = 0.8529
27/27 [==============================] - 1s 33ms/step - d_loss: 0.9403 - g_loss: 2.6738 - d_accuracy: 0.8635
Epoch 4/500
27/27 [==============================] - ETA: 0s - d_loss: 0.7480 - g_loss: 2.3182 - d_accuracy: 0.8875Epoch 4: D Accuracy = 0.8971
27/27 [==============================] - 1s 32ms/step - d_loss: 0.7443 - g_loss: 2.3541 - d_accuracy: 0.8879
Epoch 5/500
27/27 [==============================] - ETA: 0s - d_loss: 0.5100 - g_loss: 3.3879 - d_accuracy: 0.9630Epoch 5: D Accuracy = 0.9853
27/27 [==============================] - 1s 33ms/step - d_loss: 0.5077 - g_loss: 3.4101 - d_accuracy: 0.9638
Epoch 6/500
27/27 [==============================] - ETA: 0s - d_loss: 0.4399 - g_loss: 4.1510 - d_accuracy: 0.9808Epoch 6: D Accuracy = 0.9926
27/27 [==============================] - 1s 32ms/step - d_loss: 0.4382 - g_loss: 4.1782 - d_accuracy: 0.9812
Epoch 7/500
27/27 [==============================] - ETA: 0s - d_loss: 0.4090 - g_loss: 4.4495 - d_accuracy: 0.9885Epoch 7: D Accuracy = 0.9779
27/27 [==============================] - 1s 32ms/step - d_loss: 0.4095 - g_loss: 4.4455 - d_accuracy: 0.9881
Epoch 8/500
27/27 [==============================] - ETA: 0s - d_loss: 0.4033 - g_loss: 4.5736 - d_accuracy: 0.9879Epoch 8: D Accuracy = 0.9779
27/27 [==============================] - 1s 32ms/step - d_loss: 0.4040 - g_loss: 4.5883 - d_accuracy: 0.9875
Epoch 9/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1337 - g_loss: 2.0015 - d_accuracy: 0.7849Epoch 9: D Accuracy = 0.7206
27/27 [==============================] - 1s 32ms/step - d_loss: 1.1347 - g_loss: 1.9854 - d_accuracy: 0.7826
Epoch 10/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2020 - g_loss: 1.1813 - d_accuracy: 0.6957
Epoch 10: Generator loss improved from 1.4738 to 1.0046. Saving weights.
Epoch 10: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2000 - g_loss: 1.1750 - d_accuracy: 0.6979
Epoch 11/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1784 - g_loss: 1.1461 - d_accuracy: 0.7003
Epoch 11: Generator loss improved from 1.0046 to 0.9565. Saving weights.
Epoch 11: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1772 - g_loss: 1.1393 - d_accuracy: 0.7015
Epoch 12/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1749 - g_loss: 1.1670 - d_accuracy: 0.6979Epoch 12: D Accuracy = 0.6912
27/27 [==============================] - 1s 32ms/step - d_loss: 1.1740 - g_loss: 1.1604 - d_accuracy: 0.6977
Epoch 13/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1807 - g_loss: 1.1573 - d_accuracy: 0.7036Epoch 13: D Accuracy = 0.7132
27/27 [==============================] - 1s 32ms/step - d_loss: 1.1810 - g_loss: 1.1588 - d_accuracy: 0.7040
Epoch 14/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1577 - g_loss: 1.1709 - d_accuracy: 0.7129
Epoch 14: Generator loss improved from 0.9565 to 0.8932. Saving weights.
Epoch 14: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1600 - g_loss: 1.1610 - d_accuracy: 0.7137
Epoch 15/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1286 - g_loss: 1.2353 - d_accuracy: 0.7254Epoch 15: D Accuracy = 0.6985
27/27 [==============================] - 1s 32ms/step - d_loss: 1.1283 - g_loss: 1.2429 - d_accuracy: 0.7244
Epoch 16/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0858 - g_loss: 1.3060 - d_accuracy: 0.7458Epoch 16: D Accuracy = 0.8309
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0823 - g_loss: 1.3008 - d_accuracy: 0.7488
Epoch 17/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0510 - g_loss: 1.3715 - d_accuracy: 0.7631Epoch 17: D Accuracy = 0.8456
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0484 - g_loss: 1.3608 - d_accuracy: 0.7660
Epoch 18/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0106 - g_loss: 1.4503 - d_accuracy: 0.7799
Epoch 18: Generator loss improved from 0.8932 to 0.8818. Saving weights.
Epoch 18: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0133 - g_loss: 1.4300 - d_accuracy: 0.7799
Epoch 19/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0398 - g_loss: 1.5129 - d_accuracy: 0.7670Epoch 19: D Accuracy = 0.8603
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0356 - g_loss: 1.5088 - d_accuracy: 0.7703
Epoch 20/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9752 - g_loss: 1.5060 - d_accuracy: 0.7934Epoch 20: D Accuracy = 0.8015
27/27 [==============================] - 1s 32ms/step - d_loss: 0.9726 - g_loss: 1.5134 - d_accuracy: 0.7937
Epoch 21/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0298 - g_loss: 1.5028 - d_accuracy: 0.7709Epoch 21: D Accuracy = 0.7647
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0322 - g_loss: 1.4821 - d_accuracy: 0.7707
Epoch 22/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0208 - g_loss: 1.4961 - d_accuracy: 0.7678Epoch 22: D Accuracy = 0.6691
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0224 - g_loss: 1.5096 - d_accuracy: 0.7643
Epoch 23/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0695 - g_loss: 1.4359 - d_accuracy: 0.7499Epoch 23: D Accuracy = 0.7721
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0687 - g_loss: 1.4287 - d_accuracy: 0.7507
Epoch 24/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0753 - g_loss: 1.4422 - d_accuracy: 0.7404Epoch 24: D Accuracy = 0.7574
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0734 - g_loss: 1.4376 - d_accuracy: 0.7410
Epoch 25/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0696 - g_loss: 1.4247 - d_accuracy: 0.7440Epoch 25: D Accuracy = 0.6029
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0757 - g_loss: 1.4434 - d_accuracy: 0.7389
Epoch 26/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0660 - g_loss: 1.3769 - d_accuracy: 0.7494Epoch 26: D Accuracy = 0.7647
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0661 - g_loss: 1.3651 - d_accuracy: 0.7499
Epoch 27/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0617 - g_loss: 1.3987 - d_accuracy: 0.7551Epoch 27: D Accuracy = 0.7426
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0640 - g_loss: 1.3827 - d_accuracy: 0.7546
Epoch 28/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0635 - g_loss: 1.3835 - d_accuracy: 0.7530Epoch 28: D Accuracy = 0.7721
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0609 - g_loss: 1.3964 - d_accuracy: 0.7537
Epoch 29/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0880 - g_loss: 1.3605 - d_accuracy: 0.7447Epoch 29: D Accuracy = 0.8309
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0830 - g_loss: 1.3578 - d_accuracy: 0.7478
Epoch 30/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0429 - g_loss: 1.3823 - d_accuracy: 0.7635Epoch 30: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0460 - g_loss: 1.3651 - d_accuracy: 0.7620
Epoch 31/500

27/27 [==============================] - ETA: 0s - d_loss: 1.0608 - g_loss: 1.3912 - d_accuracy: 0.7479Epoch 31: D Accuracy = 0.7132
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0641 - g_loss: 1.3763 - d_accuracy: 0.7467
Epoch 32/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0483 - g_loss: 1.3972 - d_accuracy: 0.7597Epoch 32: D Accuracy = 0.7941
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0460 - g_loss: 1.3978 - d_accuracy: 0.7610
Epoch 33/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0628 - g_loss: 1.3930 - d_accuracy: 0.7559Epoch 33: D Accuracy = 0.7500
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0661 - g_loss: 1.3755 - d_accuracy: 0.7557
Epoch 34/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0510 - g_loss: 1.4258 - d_accuracy: 0.7570Epoch 34: D Accuracy = 0.7868
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0500 - g_loss: 1.4219 - d_accuracy: 0.7581
Epoch 35/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0509 - g_loss: 1.4083 - d_accuracy: 0.7631Epoch 35: D Accuracy = 0.8235
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0512 - g_loss: 1.3985 - d_accuracy: 0.7653
Epoch 36/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0658 - g_loss: 1.4307 - d_accuracy: 0.7505Epoch 36: D Accuracy = 0.7721
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0626 - g_loss: 1.4373 - d_accuracy: 0.7513
Epoch 37/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0560 - g_loss: 1.3963 - d_accuracy: 0.7611
Epoch 37: Generator loss improved from 0.8818 to 0.8202. Saving weights.
Epoch 37: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0622 - g_loss: 1.3758 - d_accuracy: 0.7584
Epoch 38/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0484 - g_loss: 1.4354 - d_accuracy: 0.7593Epoch 38: D Accuracy = 0.7941
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0478 - g_loss: 1.4224 - d_accuracy: 0.7605
Epoch 39/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0427 - g_loss: 1.4475 - d_accuracy: 0.7670Epoch 39: D Accuracy = 0.7647
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0411 - g_loss: 1.4487 - d_accuracy: 0.7670
Epoch 40/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0387 - g_loss: 1.4104 - d_accuracy: 0.7670Epoch 40: D Accuracy = 0.7794
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0377 - g_loss: 1.4017 - d_accuracy: 0.7674
Epoch 41/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0715 - g_loss: 1.4462 - d_accuracy: 0.7451Epoch 41: D Accuracy = 0.7206
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0692 - g_loss: 1.4534 - d_accuracy: 0.7443
Epoch 42/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0492 - g_loss: 1.4262 - d_accuracy: 0.7578Epoch 42: D Accuracy = 0.7132
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0494 - g_loss: 1.4421 - d_accuracy: 0.7562
Epoch 43/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0840 - g_loss: 1.4190 - d_accuracy: 0.7393Epoch 43: D Accuracy = 0.7500
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0820 - g_loss: 1.4148 - d_accuracy: 0.7397
Epoch 44/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0524 - g_loss: 1.3971 - d_accuracy: 0.7597Epoch 44: D Accuracy = 0.7279
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0562 - g_loss: 1.3819 - d_accuracy: 0.7586
Epoch 45/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0725 - g_loss: 1.4290 - d_accuracy: 0.7481Epoch 45: D Accuracy = 0.8309
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0679 - g_loss: 1.4324 - d_accuracy: 0.7510
Epoch 46/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0590 - g_loss: 1.4037 - d_accuracy: 0.7597Epoch 46: D Accuracy = 0.8088
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0564 - g_loss: 1.3997 - d_accuracy: 0.7615
Epoch 47/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0886 - g_loss: 1.4067 - d_accuracy: 0.7434Epoch 47: D Accuracy = 0.7941
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0855 - g_loss: 1.3967 - d_accuracy: 0.7452
Epoch 48/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0398 - g_loss: 1.4087 - d_accuracy: 0.7604Epoch 48: D Accuracy = 0.6471
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0431 - g_loss: 1.4259 - d_accuracy: 0.7563
Epoch 49/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1103 - g_loss: 1.3816 - d_accuracy: 0.7388Epoch 49: D Accuracy = 0.7941
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1066 - g_loss: 1.3776 - d_accuracy: 0.7407
Epoch 50/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0815 - g_loss: 1.4054 - d_accuracy: 0.7423Epoch 50: D Accuracy = 0.7206
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0809 - g_loss: 1.4270 - d_accuracy: 0.7415
Epoch 51/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0738 - g_loss: 1.3880 - d_accuracy: 0.7504Epoch 51: D Accuracy = 0.7794
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0741 - g_loss: 1.3790 - d_accuracy: 0.7514
Epoch 52/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0645 - g_loss: 1.4044 - d_accuracy: 0.7519Epoch 52: D Accuracy = 0.8162
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0603 - g_loss: 1.4048 - d_accuracy: 0.7542
Epoch 53/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0669 - g_loss: 1.4358 - d_accuracy: 0.7482Epoch 53: D Accuracy = 0.8235
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0642 - g_loss: 1.4314 - d_accuracy: 0.7509
Epoch 54/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0982 - g_loss: 1.4281 - d_accuracy: 0.7383Epoch 54: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0962 - g_loss: 1.4300 - d_accuracy: 0.7387
Epoch 55/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0826 - g_loss: 1.3820 - d_accuracy: 0.7361Epoch 55: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0826 - g_loss: 1.3770 - d_accuracy: 0.7352
Epoch 56/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0786 - g_loss: 1.4044 - d_accuracy: 0.7425Epoch 56: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0804 - g_loss: 1.3873 - d_accuracy: 0.7430
Epoch 57/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0630 - g_loss: 1.4010 - d_accuracy: 0.7452Epoch 57: D Accuracy = 0.6985
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0647 - g_loss: 1.3957 - d_accuracy: 0.7435
Epoch 58/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0943 - g_loss: 1.4214 - d_accuracy: 0.7387Epoch 58: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0964 - g_loss: 1.4055 - d_accuracy: 0.7399
Epoch 59/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0592 - g_loss: 1.4132 - d_accuracy: 0.7569Epoch 59: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0593 - g_loss: 1.4036 - d_accuracy: 0.7579
Epoch 60/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0818 - g_loss: 1.4409 - d_accuracy: 0.7405Epoch 60: D Accuracy = 0.6029
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0889 - g_loss: 1.4712 - d_accuracy: 0.7356
Epoch 61/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0636 - g_loss: 1.4001 - d_accuracy: 0.7537Epoch 61: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0617 - g_loss: 1.3960 - d_accuracy: 0.7549
Epoch 62/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0777 - g_loss: 1.4241 - d_accuracy: 0.7472Epoch 62: D Accuracy = 0.7868
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0735 - g_loss: 1.4289 - d_accuracy: 0.7486

Epoch 63/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1052 - g_loss: 1.4180 - d_accuracy: 0.7284Epoch 63: D Accuracy = 0.7059
27/27 [==============================] - 1s 32ms/step - d_loss: 1.1034 - g_loss: 1.4256 - d_accuracy: 0.7276
Epoch 64/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0851 - g_loss: 1.3840 - d_accuracy: 0.7383Epoch 64: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0819 - g_loss: 1.3817 - d_accuracy: 0.7401
Epoch 65/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0968 - g_loss: 1.3752 - d_accuracy: 0.7293Epoch 65: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0979 - g_loss: 1.3605 - d_accuracy: 0.7303
Epoch 66/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1031 - g_loss: 1.3874 - d_accuracy: 0.7275Epoch 66: D Accuracy = 0.7868
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0974 - g_loss: 1.3923 - d_accuracy: 0.7296
Epoch 67/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0903 - g_loss: 1.3351 - d_accuracy: 0.7376Epoch 67: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0938 - g_loss: 1.3271 - d_accuracy: 0.7368
Epoch 68/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1086 - g_loss: 1.3868 - d_accuracy: 0.7332Epoch 68: D Accuracy = 0.8235
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1046 - g_loss: 1.3834 - d_accuracy: 0.7364
Epoch 69/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1362 - g_loss: 1.3351 - d_accuracy: 0.7185Epoch 69: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1356 - g_loss: 1.3308 - d_accuracy: 0.7196
Epoch 70/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1298 - g_loss: 1.3200 - d_accuracy: 0.7128Epoch 70: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1253 - g_loss: 1.3272 - d_accuracy: 0.7144
Epoch 71/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0937 - g_loss: 1.3561 - d_accuracy: 0.7379Epoch 71: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0945 - g_loss: 1.3712 - d_accuracy: 0.7368
Epoch 72/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0964 - g_loss: 1.3376 - d_accuracy: 0.7373Epoch 72: D Accuracy = 0.7794
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0966 - g_loss: 1.3281 - d_accuracy: 0.7388
Epoch 73/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1430 - g_loss: 1.3666 - d_accuracy: 0.7190Epoch 73: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1400 - g_loss: 1.3677 - d_accuracy: 0.7204
Epoch 74/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1176 - g_loss: 1.3055 - d_accuracy: 0.7258Epoch 74: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1164 - g_loss: 1.3074 - d_accuracy: 0.7269
Epoch 75/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1253 - g_loss: 1.3594 - d_accuracy: 0.7231Epoch 75: D Accuracy = 0.6029
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1289 - g_loss: 1.3659 - d_accuracy: 0.7188
Epoch 76/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1214 - g_loss: 1.3013 - d_accuracy: 0.7264Epoch 76: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1196 - g_loss: 1.2955 - d_accuracy: 0.7280
Epoch 77/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1081 - g_loss: 1.3097 - d_accuracy: 0.7356Epoch 77: D Accuracy = 0.7941
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1061 - g_loss: 1.3068 - d_accuracy: 0.7377
Epoch 78/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1057 - g_loss: 1.3135 - d_accuracy: 0.7270
Epoch 78: Generator loss improved from 0.8202 to 0.7898. Saving weights.
Epoch 78: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1080 - g_loss: 1.2948 - d_accuracy: 0.7271
Epoch 79/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1723 - g_loss: 1.3459 - d_accuracy: 0.7060Epoch 79: D Accuracy = 0.8309
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1657 - g_loss: 1.3484 - d_accuracy: 0.7104
Epoch 80/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0958 - g_loss: 1.3105 - d_accuracy: 0.7372Epoch 80: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0991 - g_loss: 1.3179 - d_accuracy: 0.7342
Epoch 81/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1283 - g_loss: 1.3347 - d_accuracy: 0.7267Epoch 81: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1253 - g_loss: 1.3308 - d_accuracy: 0.7280
Epoch 82/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1481 - g_loss: 1.3065 - d_accuracy: 0.7036Epoch 82: D Accuracy = 0.8603
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1425 - g_loss: 1.3052 - d_accuracy: 0.7092
Epoch 83/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0900 - g_loss: 1.3370 - d_accuracy: 0.7364Epoch 83: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0962 - g_loss: 1.3444 - d_accuracy: 0.7322
Epoch 84/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1263 - g_loss: 1.3187 - d_accuracy: 0.7230Epoch 84: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1278 - g_loss: 1.3304 - d_accuracy: 0.7216
Epoch 85/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1367 - g_loss: 1.2916 - d_accuracy: 0.7190Epoch 85: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1363 - g_loss: 1.3023 - d_accuracy: 0.7178
Epoch 86/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0928 - g_loss: 1.2899 - d_accuracy: 0.7387Epoch 86: D Accuracy = 0.7353
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0908 - g_loss: 1.3028 - d_accuracy: 0.7386
Epoch 87/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1332 - g_loss: 1.3487 - d_accuracy: 0.7158Epoch 87: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1340 - g_loss: 1.3551 - d_accuracy: 0.7157
Epoch 88/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1304 - g_loss: 1.3029 - d_accuracy: 0.7168Epoch 88: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1294 - g_loss: 1.2964 - d_accuracy: 0.7185
Epoch 89/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1100 - g_loss: 1.3099 - d_accuracy: 0.7262Epoch 89: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1060 - g_loss: 1.3179 - d_accuracy: 0.7279
Epoch 90/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1454 - g_loss: 1.3118 - d_accuracy: 0.7128Epoch 90: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1456 - g_loss: 1.3114 - d_accuracy: 0.7125
Epoch 91/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1221 - g_loss: 1.3240 - d_accuracy: 0.7219Epoch 91: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1202 - g_loss: 1.3202 - d_accuracy: 0.7229
Epoch 92/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1497 - g_loss: 1.2817 - d_accuracy: 0.7162Epoch 92: D Accuracy = 0.8309
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1453 - g_loss: 1.2781 - d_accuracy: 0.7203
Epoch 93/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1080 - g_loss: 1.3203 - d_accuracy: 0.7317
Epoch 93: Generator loss improved from 0.7898 to 0.5929. Saving weights.
Epoch 93: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.1196 - g_loss: 1.2943 - d_accuracy: 0.7284
Epoch 94/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1466 - g_loss: 1.3227 - d_accuracy: 0.7148Epoch 94: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1440 - g_loss: 1.3310 - d_accuracy: 0.7163

Epoch 95/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0906 - g_loss: 1.2937 - d_accuracy: 0.7372Epoch 95: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0923 - g_loss: 1.2884 - d_accuracy: 0.7364
Epoch 96/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1332 - g_loss: 1.3292 - d_accuracy: 0.7146Epoch 96: D Accuracy = 0.7426
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1293 - g_loss: 1.3440 - d_accuracy: 0.7156
Epoch 97/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1358 - g_loss: 1.2768 - d_accuracy: 0.7215Epoch 97: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1354 - g_loss: 1.2645 - d_accuracy: 0.7225
Epoch 98/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1390 - g_loss: 1.3170 - d_accuracy: 0.7100Epoch 98: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1334 - g_loss: 1.3226 - d_accuracy: 0.7120
Epoch 99/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1107 - g_loss: 1.3033 - d_accuracy: 0.7270Epoch 99: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1096 - g_loss: 1.3090 - d_accuracy: 0.7271
Epoch 100/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1202 - g_loss: 1.3082 - d_accuracy: 0.7201
Class 0 — Epoch 100 preview:

Epoch 100: D Accuracy = 0.7426
27/27 [==============================] - 1s 40ms/step - d_loss: 1.1179 - g_loss: 1.3081 - d_accuracy: 0.7209
Epoch 101/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0926 - g_loss: 1.3142 - d_accuracy: 0.7346Epoch 101: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0969 - g_loss: 1.2989 - d_accuracy: 0.7317
Epoch 102/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1624 - g_loss: 1.3391 - d_accuracy: 0.7055Epoch 102: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1672 - g_loss: 1.3398 - d_accuracy: 0.7023
Epoch 103/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1066 - g_loss: 1.3111 - d_accuracy: 0.7339Epoch 103: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1034 - g_loss: 1.3142 - d_accuracy: 0.7353
Epoch 104/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1187 - g_loss: 1.3241 - d_accuracy: 0.7167Epoch 104: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1226 - g_loss: 1.3288 - d_accuracy: 0.7144
Epoch 105/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1315 - g_loss: 1.3051 - d_accuracy: 0.7191Epoch 105: D Accuracy = 0.6765
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1308 - g_loss: 1.3247 - d_accuracy: 0.7175
Epoch 106/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1383 - g_loss: 1.3186 - d_accuracy: 0.7135Epoch 106: D Accuracy = 0.6985
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1372 - g_loss: 1.3292 - d_accuracy: 0.7130
Epoch 107/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0906 - g_loss: 1.3252 - d_accuracy: 0.7373Epoch 107: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0880 - g_loss: 1.3325 - d_accuracy: 0.7380
Epoch 108/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1191 - g_loss: 1.3233 - d_accuracy: 0.7192Epoch 108: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1196 - g_loss: 1.3323 - d_accuracy: 0.7174
Epoch 109/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1297 - g_loss: 1.3100 - d_accuracy: 0.7122Epoch 109: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1293 - g_loss: 1.3044 - d_accuracy: 0.7125
Epoch 110/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1089 - g_loss: 1.3178 - d_accuracy: 0.7283Epoch 110: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1146 - g_loss: 1.3508 - d_accuracy: 0.7241
Epoch 111/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1230 - g_loss: 1.2979 - d_accuracy: 0.7278Epoch 111: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1204 - g_loss: 1.3047 - d_accuracy: 0.7278
Epoch 112/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1182 - g_loss: 1.3203 - d_accuracy: 0.7228Epoch 112: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1149 - g_loss: 1.3280 - d_accuracy: 0.7233
Epoch 113/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1147 - g_loss: 1.3098 - d_accuracy: 0.7245Epoch 113: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1163 - g_loss: 1.2961 - d_accuracy: 0.7254
Epoch 114/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1099 - g_loss: 1.3475 - d_accuracy: 0.7311Epoch 114: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1082 - g_loss: 1.3661 - d_accuracy: 0.7307
Epoch 115/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1291 - g_loss: 1.3260 - d_accuracy: 0.7184Epoch 115: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1247 - g_loss: 1.3266 - d_accuracy: 0.7206
Epoch 116/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1039 - g_loss: 1.3087 - d_accuracy: 0.7351Epoch 116: D Accuracy = 0.8235
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0999 - g_loss: 1.3107 - d_accuracy: 0.7382
Epoch 117/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1102 - g_loss: 1.3516 - d_accuracy: 0.7311Epoch 117: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1062 - g_loss: 1.3513 - d_accuracy: 0.7331
Epoch 118/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1105 - g_loss: 1.3150 - d_accuracy: 0.7238Epoch 118: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1094 - g_loss: 1.3082 - d_accuracy: 0.7247
Epoch 119/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1071 - g_loss: 1.3276 - d_accuracy: 0.7335Epoch 119: D Accuracy = 0.8088
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1055 - g_loss: 1.3293 - d_accuracy: 0.7362
Epoch 120/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1247 - g_loss: 1.3338 - d_accuracy: 0.7247Epoch 120: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1268 - g_loss: 1.3156 - d_accuracy: 0.7235
Epoch 121/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1317 - g_loss: 1.3767 - d_accuracy: 0.7153Epoch 121: D Accuracy = 0.5588
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1386 - g_loss: 1.4142 - d_accuracy: 0.7097
Epoch 122/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0829 - g_loss: 1.3230 - d_accuracy: 0.7395Epoch 122: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0804 - g_loss: 1.3261 - d_accuracy: 0.7407
Epoch 123/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1210 - g_loss: 1.3126 - d_accuracy: 0.7222Epoch 123: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1195 - g_loss: 1.3022 - d_accuracy: 0.7242
Epoch 124/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0910 - g_loss: 1.3345 - d_accuracy: 0.7323Epoch 124: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0910 - g_loss: 1.3287 - d_accuracy: 0.7330
Epoch 125/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1231 - g_loss: 1.3494 - d_accuracy: 0.7218Epoch 125: D Accuracy = 0.8015
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1184 - g_loss: 1.3469 - d_accuracy: 0.7247
Epoch 126/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0682 - g_loss: 1.3463 - d_accuracy: 0.7493Epoch 126: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0709 - g_loss: 1.3354 - d_accuracy: 0.7483
Epoch 127/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1195 - g_loss: 1.3734 - d_accuracy: 0.7169Epoch 127: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1185 - g_loss: 1.3855 - d_accuracy: 0.7171
Epoch 128/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1007 - g_loss: 1.3457 - d_accuracy: 0.7306Epoch 128: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1006 - g_loss: 1.3424 - d_accuracy: 0.7308
Epoch 129/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0999 - g_loss: 1.3619 - d_accuracy: 0.7314Epoch 129: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1002 - g_loss: 1.3741 - d_accuracy: 0.7289
Epoch 130/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1160 - g_loss: 1.3686 - d_accuracy: 0.7238Epoch 130: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1207 - g_loss: 1.3782 - d_accuracy: 0.7198
Epoch 131/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1151 - g_loss: 1.3654 - d_accuracy: 0.7234Epoch 131: D Accuracy = 0.8015
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1127 - g_loss: 1.3566 - d_accuracy: 0.7262
Epoch 132/500

27/27 [==============================] - ETA: 0s - d_loss: 1.0911 - g_loss: 1.3590 - d_accuracy: 0.7389Epoch 132: D Accuracy = 0.7941
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0877 - g_loss: 1.3605 - d_accuracy: 0.7409
Epoch 133/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1083 - g_loss: 1.3358 - d_accuracy: 0.7342Epoch 133: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1061 - g_loss: 1.3334 - d_accuracy: 0.7350
Epoch 134/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0829 - g_loss: 1.3598 - d_accuracy: 0.7403Epoch 134: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0883 - g_loss: 1.3785 - d_accuracy: 0.7359
Epoch 135/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1169 - g_loss: 1.3629 - d_accuracy: 0.7207Epoch 135: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1157 - g_loss: 1.3625 - d_accuracy: 0.7212
Epoch 136/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0896 - g_loss: 1.3746 - d_accuracy: 0.7377Epoch 136: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0876 - g_loss: 1.3776 - d_accuracy: 0.7386
Epoch 137/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0759 - g_loss: 1.3766 - d_accuracy: 0.7441Epoch 137: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0820 - g_loss: 1.3693 - d_accuracy: 0.7412
Epoch 138/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0889 - g_loss: 1.3451 - d_accuracy: 0.7332Epoch 138: D Accuracy = 0.6985
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0906 - g_loss: 1.3384 - d_accuracy: 0.7320
Epoch 139/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0945 - g_loss: 1.3617 - d_accuracy: 0.7366Epoch 139: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0916 - g_loss: 1.3695 - d_accuracy: 0.7365
Epoch 140/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1105 - g_loss: 1.3526 - d_accuracy: 0.7236Epoch 140: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1100 - g_loss: 1.3467 - d_accuracy: 0.7251
Epoch 141/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0659 - g_loss: 1.3702 - d_accuracy: 0.7507Epoch 141: D Accuracy = 0.6397
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0699 - g_loss: 1.3774 - d_accuracy: 0.7467
Epoch 142/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0793 - g_loss: 1.4041 - d_accuracy: 0.7424Epoch 142: D Accuracy = 0.5809
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0887 - g_loss: 1.4257 - d_accuracy: 0.7367
Epoch 143/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1431 - g_loss: 1.4030 - d_accuracy: 0.7203Epoch 143: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1481 - g_loss: 1.3791 - d_accuracy: 0.7203
Epoch 144/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0595 - g_loss: 1.3688 - d_accuracy: 0.7543Epoch 144: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0601 - g_loss: 1.3636 - d_accuracy: 0.7554
Epoch 145/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0751 - g_loss: 1.3805 - d_accuracy: 0.7455Epoch 145: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0729 - g_loss: 1.3846 - d_accuracy: 0.7452
Epoch 146/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1130 - g_loss: 1.3463 - d_accuracy: 0.7277Epoch 146: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1114 - g_loss: 1.3387 - d_accuracy: 0.7288
Epoch 147/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0696 - g_loss: 1.3634 - d_accuracy: 0.7499Epoch 147: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0677 - g_loss: 1.3811 - d_accuracy: 0.7491
Epoch 148/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0894 - g_loss: 1.4050 - d_accuracy: 0.7339Epoch 148: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0894 - g_loss: 1.4203 - d_accuracy: 0.7331
Epoch 149/500
27/27 [==============================] - ETA: 0s - d_loss: 1.1118 - g_loss: 1.3551 - d_accuracy: 0.7288Epoch 149: D Accuracy = 0.6471
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1160 - g_loss: 1.3395 - d_accuracy: 0.7259
Epoch 150/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0988 - g_loss: 1.3867 - d_accuracy: 0.7244Epoch 150: D Accuracy = 0.6324
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1018 - g_loss: 1.3939 - d_accuracy: 0.7211
Epoch 151/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0879 - g_loss: 1.3666 - d_accuracy: 0.7391Epoch 151: D Accuracy = 0.6324
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0972 - g_loss: 1.3925 - d_accuracy: 0.7353
Epoch 152/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0830 - g_loss: 1.3692 - d_accuracy: 0.7369Epoch 152: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0813 - g_loss: 1.3742 - d_accuracy: 0.7366
Epoch 153/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0863 - g_loss: 1.3752 - d_accuracy: 0.7389Epoch 153: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0858 - g_loss: 1.3629 - d_accuracy: 0.7404
Epoch 154/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0982 - g_loss: 1.4001 - d_accuracy: 0.7274Epoch 154: D Accuracy = 0.7426
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0971 - g_loss: 1.3987 - d_accuracy: 0.7280
Epoch 155/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0769 - g_loss: 1.3753 - d_accuracy: 0.7503Epoch 155: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0768 - g_loss: 1.3716 - d_accuracy: 0.7498
Epoch 156/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0756 - g_loss: 1.3797 - d_accuracy: 0.7465Epoch 156: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0849 - g_loss: 1.3564 - d_accuracy: 0.7445
Epoch 157/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0875 - g_loss: 1.3886 - d_accuracy: 0.7359Epoch 157: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0851 - g_loss: 1.3956 - d_accuracy: 0.7375
Epoch 158/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0757 - g_loss: 1.4067 - d_accuracy: 0.7379Epoch 158: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0744 - g_loss: 1.4110 - d_accuracy: 0.7373
Epoch 159/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0710 - g_loss: 1.3885 - d_accuracy: 0.7459Epoch 159: D Accuracy = 0.8382
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0685 - g_loss: 1.3782 - d_accuracy: 0.7492
Epoch 160/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0828 - g_loss: 1.3903 - d_accuracy: 0.7433Epoch 160: D Accuracy = 0.6985
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0859 - g_loss: 1.3692 - d_accuracy: 0.7417
Epoch 161/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0663 - g_loss: 1.4086 - d_accuracy: 0.7488Epoch 161: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0665 - g_loss: 1.4288 - d_accuracy: 0.7454
Epoch 162/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0835 - g_loss: 1.3843 - d_accuracy: 0.7366
Epoch 162: Generator loss improved from 0.5929 to 0.5337. Saving weights.
Epoch 162: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0964 - g_loss: 1.3539 - d_accuracy: 0.7323
Epoch 163/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0590 - g_loss: 1.4525 - d_accuracy: 0.7493Epoch 163: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0639 - g_loss: 1.4748 - d_accuracy: 0.7448

Epoch 164/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0692 - g_loss: 1.3902 - d_accuracy: 0.7422Epoch 164: D Accuracy = 0.6765
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0722 - g_loss: 1.3994 - d_accuracy: 0.7399
Epoch 165/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0559 - g_loss: 1.3998 - d_accuracy: 0.7562Epoch 165: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0555 - g_loss: 1.3966 - d_accuracy: 0.7567
Epoch 166/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0884 - g_loss: 1.4238 - d_accuracy: 0.7311Epoch 166: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0890 - g_loss: 1.4036 - d_accuracy: 0.7310
Epoch 167/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0733 - g_loss: 1.3859 - d_accuracy: 0.7429Epoch 167: D Accuracy = 0.7426
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0766 - g_loss: 1.3690 - d_accuracy: 0.7429
Epoch 168/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0623 - g_loss: 1.4225 - d_accuracy: 0.7480Epoch 168: D Accuracy = 0.7574
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0637 - g_loss: 1.4150 - d_accuracy: 0.7483
Epoch 169/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0857 - g_loss: 1.4193 - d_accuracy: 0.7376Epoch 169: D Accuracy = 0.7353
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0893 - g_loss: 1.4083 - d_accuracy: 0.7375
Epoch 170/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0664 - g_loss: 1.4235 - d_accuracy: 0.7467Epoch 170: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0650 - g_loss: 1.4188 - d_accuracy: 0.7482
Epoch 171/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0523 - g_loss: 1.4270 - d_accuracy: 0.7531Epoch 171: D Accuracy = 0.6471
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0554 - g_loss: 1.4483 - d_accuracy: 0.7493
Epoch 172/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0511 - g_loss: 1.4328 - d_accuracy: 0.7528Epoch 172: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0520 - g_loss: 1.4236 - d_accuracy: 0.7535
Epoch 173/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0763 - g_loss: 1.4232 - d_accuracy: 0.7431Epoch 173: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0746 - g_loss: 1.4207 - d_accuracy: 0.7447
Epoch 174/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0740 - g_loss: 1.4359 - d_accuracy: 0.7439Epoch 174: D Accuracy = 0.7794
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0738 - g_loss: 1.4226 - d_accuracy: 0.7451
Epoch 175/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0611 - g_loss: 1.4367 - d_accuracy: 0.7458Epoch 175: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0570 - g_loss: 1.4462 - d_accuracy: 0.7467
Epoch 176/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0606 - g_loss: 1.4605 - d_accuracy: 0.7479Epoch 176: D Accuracy = 0.8309
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0544 - g_loss: 1.4664 - d_accuracy: 0.7509
Epoch 177/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0324 - g_loss: 1.4685 - d_accuracy: 0.7645Epoch 177: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0336 - g_loss: 1.4823 - d_accuracy: 0.7616
Epoch 178/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0992 - g_loss: 1.4335 - d_accuracy: 0.7329Epoch 178: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.1029 - g_loss: 1.4594 - d_accuracy: 0.7301
Epoch 179/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0549 - g_loss: 1.4008 - d_accuracy: 0.7531Epoch 179: D Accuracy = 0.7426
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0545 - g_loss: 1.3985 - d_accuracy: 0.7527
Epoch 180/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0186 - g_loss: 1.4381 - d_accuracy: 0.7726Epoch 180: D Accuracy = 0.7941
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0169 - g_loss: 1.4404 - d_accuracy: 0.7734
Epoch 181/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0982 - g_loss: 1.4483 - d_accuracy: 0.7308Epoch 181: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0982 - g_loss: 1.4459 - d_accuracy: 0.7305
Epoch 182/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0516 - g_loss: 1.4443 - d_accuracy: 0.7550Epoch 182: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0491 - g_loss: 1.4653 - d_accuracy: 0.7554
Epoch 183/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0553 - g_loss: 1.4406 - d_accuracy: 0.7514Epoch 183: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0515 - g_loss: 1.4560 - d_accuracy: 0.7514
Epoch 184/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0656 - g_loss: 1.4769 - d_accuracy: 0.7455Epoch 184: D Accuracy = 0.6985
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0649 - g_loss: 1.4854 - d_accuracy: 0.7438
Epoch 185/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0466 - g_loss: 1.4435 - d_accuracy: 0.7586Epoch 185: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0501 - g_loss: 1.4262 - d_accuracy: 0.7568
Epoch 186/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0815 - g_loss: 1.4773 - d_accuracy: 0.7378Epoch 186: D Accuracy = 0.7132
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0823 - g_loss: 1.4729 - d_accuracy: 0.7369
Epoch 187/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0649 - g_loss: 1.4580 - d_accuracy: 0.7507Epoch 187: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0662 - g_loss: 1.4664 - d_accuracy: 0.7481
Epoch 188/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0239 - g_loss: 1.4430 - d_accuracy: 0.7745Epoch 188: D Accuracy = 0.8088
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0221 - g_loss: 1.4452 - d_accuracy: 0.7757
Epoch 189/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0606 - g_loss: 1.4454 - d_accuracy: 0.7487Epoch 189: D Accuracy = 0.7426
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0592 - g_loss: 1.4525 - d_accuracy: 0.7485
Epoch 190/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0300 - g_loss: 1.4424 - d_accuracy: 0.7664Epoch 190: D Accuracy = 0.8382
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0282 - g_loss: 1.4330 - d_accuracy: 0.7690
Epoch 191/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0766 - g_loss: 1.4619 - d_accuracy: 0.7423Epoch 191: D Accuracy = 0.7721
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0771 - g_loss: 1.4536 - d_accuracy: 0.7433
Epoch 192/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0714 - g_loss: 1.4980 - d_accuracy: 0.7448Epoch 192: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0668 - g_loss: 1.5091 - d_accuracy: 0.7455
Epoch 193/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0238 - g_loss: 1.4524 - d_accuracy: 0.7696Epoch 193: D Accuracy = 0.8015
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0221 - g_loss: 1.4572 - d_accuracy: 0.7707
Epoch 194/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0340 - g_loss: 1.4926 - d_accuracy: 0.7635Epoch 194: D Accuracy = 0.7132
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0411 - g_loss: 1.4710 - d_accuracy: 0.7617
Epoch 195/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0078 - g_loss: 1.4934 - d_accuracy: 0.7763Epoch 195: D Accuracy = 0.7794
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0052 - g_loss: 1.4982 - d_accuracy: 0.7764

Epoch 196/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0755 - g_loss: 1.4812 - d_accuracy: 0.7442Epoch 196: D Accuracy = 0.7500
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0735 - g_loss: 1.4852 - d_accuracy: 0.7444
Epoch 197/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0252 - g_loss: 1.4794 - d_accuracy: 0.7650Epoch 197: D Accuracy = 0.7059
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0285 - g_loss: 1.4638 - d_accuracy: 0.7629
Epoch 198/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0952 - g_loss: 1.5110 - d_accuracy: 0.7286Epoch 198: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0922 - g_loss: 1.5202 - d_accuracy: 0.7265
Epoch 199/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0145 - g_loss: 1.4617 - d_accuracy: 0.7766Epoch 199: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0138 - g_loss: 1.4621 - d_accuracy: 0.7762
Epoch 200/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0224 - g_loss: 1.4729 - d_accuracy: 0.7723
Class 0 — Epoch 200 preview:

Epoch 200: D Accuracy = 0.8603
27/27 [==============================] - 1s 42ms/step - d_loss: 1.0186 - g_loss: 1.4656 - d_accuracy: 0.7755
Epoch 201/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0181 - g_loss: 1.5039 - d_accuracy: 0.7735Epoch 201: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0211 - g_loss: 1.5040 - d_accuracy: 0.7719
Epoch 202/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0242 - g_loss: 1.5306 - d_accuracy: 0.7647Epoch 202: D Accuracy = 0.7868
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0231 - g_loss: 1.5385 - d_accuracy: 0.7655
Epoch 203/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0464 - g_loss: 1.5174 - d_accuracy: 0.7557Epoch 203: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0445 - g_loss: 1.5143 - d_accuracy: 0.7563
Epoch 204/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0365 - g_loss: 1.4905 - d_accuracy: 0.7628Epoch 204: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0360 - g_loss: 1.4957 - d_accuracy: 0.7632
Epoch 205/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0577 - g_loss: 1.5009 - d_accuracy: 0.7543Epoch 205: D Accuracy = 0.8382
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0526 - g_loss: 1.4992 - d_accuracy: 0.7573
Epoch 206/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0645 - g_loss: 1.4983 - d_accuracy: 0.7507Epoch 206: D Accuracy = 0.7132
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0628 - g_loss: 1.5098 - d_accuracy: 0.7493
Epoch 207/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0230 - g_loss: 1.4853 - d_accuracy: 0.7701Epoch 207: D Accuracy = 0.8603
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0198 - g_loss: 1.4841 - d_accuracy: 0.7734
Epoch 208/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0691 - g_loss: 1.5133 - d_accuracy: 0.7416Epoch 208: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0684 - g_loss: 1.5184 - d_accuracy: 0.7424
Epoch 209/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0107 - g_loss: 1.4975 - d_accuracy: 0.7775Epoch 209: D Accuracy = 0.8529
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0063 - g_loss: 1.5017 - d_accuracy: 0.7802
Epoch 210/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0326 - g_loss: 1.4963 - d_accuracy: 0.7676Epoch 210: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0329 - g_loss: 1.4919 - d_accuracy: 0.7675
Epoch 211/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0367 - g_loss: 1.5069 - d_accuracy: 0.7593Epoch 211: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0385 - g_loss: 1.5152 - d_accuracy: 0.7561
Epoch 212/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0615 - g_loss: 1.4711 - d_accuracy: 0.7541Epoch 212: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0611 - g_loss: 1.4553 - d_accuracy: 0.7548
Epoch 213/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0254 - g_loss: 1.5307 - d_accuracy: 0.7647Epoch 213: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0216 - g_loss: 1.5413 - d_accuracy: 0.7650
Epoch 214/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0344 - g_loss: 1.5308 - d_accuracy: 0.7555Epoch 214: D Accuracy = 0.7353
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0327 - g_loss: 1.5322 - d_accuracy: 0.7548
Epoch 215/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0010 - g_loss: 1.5296 - d_accuracy: 0.7861Epoch 215: D Accuracy = 0.8603
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9976 - g_loss: 1.5253 - d_accuracy: 0.7887
Epoch 216/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0377 - g_loss: 1.5576 - d_accuracy: 0.7550Epoch 216: D Accuracy = 0.6618
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0431 - g_loss: 1.5727 - d_accuracy: 0.7516
Epoch 217/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0494 - g_loss: 1.5074 - d_accuracy: 0.7500Epoch 217: D Accuracy = 0.7647
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0486 - g_loss: 1.5019 - d_accuracy: 0.7505
Epoch 218/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0079 - g_loss: 1.5348 - d_accuracy: 0.7747Epoch 218: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0098 - g_loss: 1.5245 - d_accuracy: 0.7743
Epoch 219/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0093 - g_loss: 1.5535 - d_accuracy: 0.7769Epoch 219: D Accuracy = 0.7353
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0105 - g_loss: 1.5455 - d_accuracy: 0.7755
Epoch 220/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9860 - g_loss: 1.5213 - d_accuracy: 0.7866Epoch 220: D Accuracy = 0.8676
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9805 - g_loss: 1.5302 - d_accuracy: 0.7895
Epoch 221/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0228 - g_loss: 1.5462 - d_accuracy: 0.7751Epoch 221: D Accuracy = 0.8456
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0178 - g_loss: 1.5434 - d_accuracy: 0.7776
Epoch 222/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9796 - g_loss: 1.5625 - d_accuracy: 0.7847Epoch 222: D Accuracy = 0.7426
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9799 - g_loss: 1.5730 - d_accuracy: 0.7832
Epoch 223/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0272 - g_loss: 1.5578 - d_accuracy: 0.7691Epoch 223: D Accuracy = 0.7647
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0270 - g_loss: 1.5486 - d_accuracy: 0.7689
Epoch 224/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9994 - g_loss: 1.5353 - d_accuracy: 0.7784Epoch 224: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0022 - g_loss: 1.5569 - d_accuracy: 0.7747
Epoch 225/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0135 - g_loss: 1.5455 - d_accuracy: 0.7727Epoch 225: D Accuracy = 0.8015
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0109 - g_loss: 1.5495 - d_accuracy: 0.7738
Epoch 226/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9990 - g_loss: 1.5536 - d_accuracy: 0.7873Epoch 226: D Accuracy = 0.8676
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9957 - g_loss: 1.5501 - d_accuracy: 0.7902
Epoch 227/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0045 - g_loss: 1.5895 - d_accuracy: 0.7836Epoch 227: D Accuracy = 0.6544
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0107 - g_loss: 1.6048 - d_accuracy: 0.7790
Epoch 228/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0128 - g_loss: 1.6343 - d_accuracy: 0.7695Epoch 228: D Accuracy = 0.7574
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0105 - g_loss: 1.6486 - d_accuracy: 0.7691
Epoch 229/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9954 - g_loss: 1.5577 - d_accuracy: 0.7840Epoch 229: D Accuracy = 0.7500
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9975 - g_loss: 1.5557 - d_accuracy: 0.7828
Epoch 230/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9978 - g_loss: 1.5621 - d_accuracy: 0.7857Epoch 230: D Accuracy = 0.7206
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0028 - g_loss: 1.5521 - d_accuracy: 0.7833
Epoch 231/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9889 - g_loss: 1.6079 - d_accuracy: 0.7793Epoch 231: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9944 - g_loss: 1.6205 - d_accuracy: 0.7746
Epoch 232/500

27/27 [==============================] - ETA: 0s - d_loss: 1.0136 - g_loss: 1.5638 - d_accuracy: 0.7755Epoch 232: D Accuracy = 0.8015
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0112 - g_loss: 1.5648 - d_accuracy: 0.7764
Epoch 233/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9995 - g_loss: 1.5826 - d_accuracy: 0.7875Epoch 233: D Accuracy = 0.7941
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9979 - g_loss: 1.5906 - d_accuracy: 0.7877
Epoch 234/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0004 - g_loss: 1.5978 - d_accuracy: 0.7773Epoch 234: D Accuracy = 0.7500
27/27 [==============================] - 1s 33ms/step - d_loss: 1.0009 - g_loss: 1.5946 - d_accuracy: 0.7764
Epoch 235/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9924 - g_loss: 1.5969 - d_accuracy: 0.7808Epoch 235: D Accuracy = 0.8235
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9914 - g_loss: 1.6004 - d_accuracy: 0.7823
Epoch 236/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0115 - g_loss: 1.5967 - d_accuracy: 0.7762Epoch 236: D Accuracy = 0.7941
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0114 - g_loss: 1.5930 - d_accuracy: 0.7769
Epoch 237/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9795 - g_loss: 1.5790 - d_accuracy: 0.7900Epoch 237: D Accuracy = 0.7941
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9806 - g_loss: 1.5649 - d_accuracy: 0.7901
Epoch 238/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0379 - g_loss: 1.5948 - d_accuracy: 0.7565Epoch 238: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0348 - g_loss: 1.6044 - d_accuracy: 0.7570
Epoch 239/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9821 - g_loss: 1.5887 - d_accuracy: 0.7841Epoch 239: D Accuracy = 0.7574
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9789 - g_loss: 1.6098 - d_accuracy: 0.7832
Epoch 240/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0059 - g_loss: 1.6134 - d_accuracy: 0.7746Epoch 240: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0109 - g_loss: 1.6271 - d_accuracy: 0.7693
Epoch 241/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0044 - g_loss: 1.5797 - d_accuracy: 0.7763Epoch 241: D Accuracy = 0.7206
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0065 - g_loss: 1.5780 - d_accuracy: 0.7743
Epoch 242/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9476 - g_loss: 1.6010 - d_accuracy: 0.8110Epoch 242: D Accuracy = 0.8162
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9503 - g_loss: 1.5888 - d_accuracy: 0.8112
Epoch 243/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9931 - g_loss: 1.6001 - d_accuracy: 0.7789Epoch 243: D Accuracy = 0.7794
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9900 - g_loss: 1.6045 - d_accuracy: 0.7789
Epoch 244/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9506 - g_loss: 1.6385 - d_accuracy: 0.8050Epoch 244: D Accuracy = 0.7426
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9535 - g_loss: 1.6402 - d_accuracy: 0.8028
Epoch 245/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0222 - g_loss: 1.6143 - d_accuracy: 0.7622Epoch 245: D Accuracy = 0.8015
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0191 - g_loss: 1.6137 - d_accuracy: 0.7636
Epoch 246/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9999 - g_loss: 1.6277 - d_accuracy: 0.7774Epoch 246: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9998 - g_loss: 1.6212 - d_accuracy: 0.7772
Epoch 247/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0061 - g_loss: 1.6484 - d_accuracy: 0.7757Epoch 247: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0085 - g_loss: 1.6351 - d_accuracy: 0.7756
Epoch 248/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9744 - g_loss: 1.6360 - d_accuracy: 0.7906Epoch 248: D Accuracy = 0.8529
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9724 - g_loss: 1.6291 - d_accuracy: 0.7928
Epoch 249/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9583 - g_loss: 1.6129 - d_accuracy: 0.7950Epoch 249: D Accuracy = 0.7574
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9595 - g_loss: 1.6185 - d_accuracy: 0.7936
Epoch 250/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9956 - g_loss: 1.6453 - d_accuracy: 0.7798Epoch 250: D Accuracy = 0.6985
27/27 [==============================] - 1s 34ms/step - d_loss: 1.0073 - g_loss: 1.6146 - d_accuracy: 0.7769
Epoch 251/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9778 - g_loss: 1.6411 - d_accuracy: 0.7876Epoch 251: D Accuracy = 0.8382
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9755 - g_loss: 1.6371 - d_accuracy: 0.7894
Epoch 252/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9384 - g_loss: 1.6927 - d_accuracy: 0.8023Epoch 252: D Accuracy = 0.7206
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9411 - g_loss: 1.7229 - d_accuracy: 0.7994
Epoch 253/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9811 - g_loss: 1.6406 - d_accuracy: 0.7868Epoch 253: D Accuracy = 0.7426
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9821 - g_loss: 1.6381 - d_accuracy: 0.7852
Epoch 254/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9814 - g_loss: 1.6546 - d_accuracy: 0.7852Epoch 254: D Accuracy = 0.8015
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9803 - g_loss: 1.6524 - d_accuracy: 0.7858
Epoch 255/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9743 - g_loss: 1.6854 - d_accuracy: 0.7887Epoch 255: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9722 - g_loss: 1.7107 - d_accuracy: 0.7881
Epoch 256/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9646 - g_loss: 1.6705 - d_accuracy: 0.7909Epoch 256: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9629 - g_loss: 1.6837 - d_accuracy: 0.7902
Epoch 257/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9529 - g_loss: 1.6707 - d_accuracy: 0.8007Epoch 257: D Accuracy = 0.8309
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9525 - g_loss: 1.6622 - d_accuracy: 0.8018
Epoch 258/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9483 - g_loss: 1.6588 - d_accuracy: 0.8021Epoch 258: D Accuracy = 0.8235
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9498 - g_loss: 1.6421 - d_accuracy: 0.8028
Epoch 259/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9740 - g_loss: 1.6627 - d_accuracy: 0.7851Epoch 259: D Accuracy = 0.7721
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9737 - g_loss: 1.6512 - d_accuracy: 0.7846
Epoch 260/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9888 - g_loss: 1.7042 - d_accuracy: 0.7797Epoch 260: D Accuracy = 0.7279
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9918 - g_loss: 1.6878 - d_accuracy: 0.7779
Epoch 261/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9802 - g_loss: 1.6817 - d_accuracy: 0.7862Epoch 261: D Accuracy = 0.7868
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9788 - g_loss: 1.6780 - d_accuracy: 0.7862
Epoch 262/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9745 - g_loss: 1.7128 - d_accuracy: 0.7890Epoch 262: D Accuracy = 0.8456
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9715 - g_loss: 1.7046 - d_accuracy: 0.7910
Epoch 263/500
27/27 [==============================] - ETA: 0s - d_loss: 0.9715 - g_loss: 1.6510 - d_accuracy: 0.7938Epoch 263: D Accuracy = 0.8235
27/27 [==============================] - 1s 34ms/step - d_loss: 0.9708 - g_loss: 1.6371 - d_accuracy: 0.7949

# === Final Save and Grid ===
def generate_and_save(dcgan_models, latent_dim=128, images_per_class=10, save_dir="16Models_DCGAN_600epochs_images"):
    os.makedirs(save_dir, exist_ok=True)
    num_classes = len(dcgan_models)
    all_images = []

    for class_idx, dcgan in dcgan_models.items():
        noise = np.random.normal(0, 1, (images_per_class, latent_dim))
        generated_images = dcgan.generator.predict(noise)
        generated_images = 0.5 * generated_images + 0.5

        class_dir = os.path.join(save_dir, f"class_{class_idx}")
        os.makedirs(class_dir, exist_ok=True)

        for i in range(images_per_class):
            img = generated_images[i, :, :, 0]
            all_images.append(img)
            plt.imsave(os.path.join(class_dir, f"img_{i+1}.png"), img, cmap='gray')

    # Save a grid of all generated images
    fig, axes = plt.subplots(num_classes, images_per_class, figsize=(images_per_class, num_classes))
    fig.suptitle("DCGAN Generated Samples per Class", fontsize=16)

    for i, ax in enumerate(axes.flat):
        ax.imshow(all_images[i], cmap='gray')
        ax.axis('off')

    grid_path = os.path.join(save_dir, "grid_160images_600epochs.png")
    plt.tight_layout()
    plt.savefig(grid_path)
    plt.show()
    print(f"Images saved to folder: {save_dir}")
    print(f"Grid saved as: {grid_path}")

def plot_loss_history(histories):
    for class_idx, history in histories.items():
        g_loss = history.history['g_loss']
        d_loss = history.history['d_loss']
        d_acc = history.history.get('d_accuracy', None)
        epochs_range = range(1, len(g_loss) + 1)

        plt.figure(figsize=(12, 4))

        plt.subplot(1, 2, 1)
        plt.plot(epochs_range, d_loss, label="Discriminator Loss")
        plt.plot(epochs_range, g_loss, label="Generator Loss")
        plt.title(f"Loss — Class {class_idx}")
        plt.xlabel("Epochs")
        plt.ylabel("Loss")
        plt.legend()
        plt.grid(True)

        if d_acc is not None:
            plt.subplot(1, 2, 2)
            plt.plot(epochs_range, d_acc, label="Discriminator Accuracy", color='green')
            plt.title(f"Discriminator Accuracy — Class {class_idx}")
            plt.xlabel("Epochs")
            plt.ylabel("Accuracy")
            plt.grid(True)
            plt.legend()

        plt.tight_layout()
        plt.show()

        
for class_idx, dcgan in dcgan_models.items():
    gen = dcgan.generator
    weight_path = os.path.join(final_save_dir, f"final_generator_class_{class_idx}.h5")
    gen.save_weights(weight_path)
    print(f"Saved generator weights for class {class_idx} to {weight_path}")

Saved generator weights for class 0 to 16Models_saved_generators_600epochs(last)\final_generator_class_0.h5
Saved generator weights for class 1 to 16Models_saved_generators_600epochs(last)\final_generator_class_1.h5
Saved generator weights for class 2 to 16Models_saved_generators_600epochs(last)\final_generator_class_2.h5
Saved generator weights for class 3 to 16Models_saved_generators_600epochs(last)\final_generator_class_3.h5
Saved generator weights for class 4 to 16Models_saved_generators_600epochs(last)\final_generator_class_4.h5
Saved generator weights for class 5 to 16Models_saved_generators_600epochs(last)\final_generator_class_5.h5
Saved generator weights for class 6 to 16Models_saved_generators_600epochs(last)\final_generator_class_6.h5
Saved generator weights for class 7 to 16Models_saved_generators_600epochs(last)\final_generator_class_7.h5
Saved generator weights for class 8 to 16Models_saved_generators_600epochs(last)\final_generator_class_8.h5
Saved generator weights for class 9 to 16Models_saved_generators_600epochs(last)\final_generator_class_9.h5
Saved generator weights for class 10 to 16Models_saved_generators_600epochs(last)\final_generator_class_10.h5
Saved generator weights for class 11 to 16Models_saved_generators_600epochs(last)\final_generator_class_11.h5
Saved generator weights for class 12 to 16Models_saved_generators_600epochs(last)\final_generator_class_12.h5
Saved generator weights for class 13 to 16Models_saved_generators_600epochs(last)\final_generator_class_13.h5
Saved generator weights for class 14 to 16Models_saved_generators_600epochs(last)\final_generator_class_14.h5
Saved generator weights for class 15 to 16Models_saved_generators_600epochs(last)\final_generator_class_15.h5

generate_and_save(dcgan_models, latent_dim=latent_dim, images_per_class=10)

1/1 [==============================] - 0s 125ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 96ms/step
1/1 [==============================] - 0s 96ms/step
WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000021A31F2EA60> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 0s 93ms/step
WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x0000021A2E96A9D0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 89ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 97ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 101ms/step
1/1 [==============================] - 0s 89ms/step

Images saved to folder: 16Models_DCGAN_600epochs_images
Grid saved as: 16Models_DCGAN_600epochs_images\grid_160images_600epochs.png

plt.plot(history.history['d_accuracy'])
plt.title("Discriminator Accuracy over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.grid(True)
plt.show()

plot_loss_history(histories)

import tensorflow as tf
import matplotlib.pyplot as plt

# Trained characters (16 letters, case-insensitive)
trained_letters = list("ABDEFGIJLNOPQTXZ")
label_to_index = {ch: idx for idx, ch in enumerate(trained_letters)}

def generate_word_from_models(dcgan_models, word, latent_dim=128):
    """
    Generate a word using DCGANs trained on 16 letters (A–Z, no case).
    Skips untrained letters.
    """
    word = word.upper()
    valid_letters = []
    valid_indices = []

    for ch in word:
        if ch in label_to_index:
            valid_letters.append(ch)
            valid_indices.append(label_to_index[ch])
        else:
            print(f"Skipping '{ch}' — not trained.")

    if not valid_letters:
        print("No valid letters to generate.")
        return

    noise = tf.random.normal((len(valid_letters), latent_dim))

    images = []
    for i, class_idx in enumerate(valid_indices):
        gen = dcgan_models[class_idx].generator
        img_tensor = gen(noise[i:i+1], training=False)
        img = (img_tensor[0, :, :, 0] * 0.5) + 0.5
        images.append(img.numpy())

    fig, axes = plt.subplots(1, len(images), figsize=(len(images)*1.5, 2))
    for ax, img, ch in zip(axes, images, valid_letters):
        ax.imshow(img, cmap='gray')
        ax.axis('off')
        ax.set_title(ch)
    plt.tight_layout()
    plt.show()

generate_word_from_models(dcgan_models, "DEEPLEARNING", latent_dim=128)
generate_word_from_models(dcgan_models, "DAAA", latent_dim=128)

Skipping 'R' — not trained.

def load_final_and_save_grid(final_save_dir, latent_dim=128, images_per_class=10, output_path="grid_final_160.png"):
    all_images = []

    for class_idx in range(16):
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(final_save_dir, f"final_generator_class_{class_idx}.h5")
        generator.load_weights(weight_path)

        noise = np.random.normal(0, 1, (images_per_class, latent_dim))
        generated = generator.predict(noise)
        generated = 0.5 * generated + 0.5

        for i in range(images_per_class):
            all_images.append(generated[i, :, :, 0])

    fig, axes = plt.subplots(16, 10, figsize=(10, 16))
    for idx, ax in enumerate(axes.flat):
        ax.imshow(all_images[idx], cmap='gray')
        ax.axis('off')
    plt.tight_layout()
    plt.suptitle("Final Generator — 160 Images", fontsize=16)
    plt.subplots_adjust(top=0.95)
    plt.savefig(output_path)
    plt.show()
    print(f"Final 160-image grid saved as: {output_path}")

load_final_and_save_grid("16Models_saved_generators_600epochs(last)", output_path="grid_final_160.png")

1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 89ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 98ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 93ms/step

Final 160-image grid saved as: grid_final_160.png

load_final_and_save_grid("16Models_saved_generators_600epochs(last)", output_path="grid_final_160(2).png")

1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 91ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 95ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 94ms/step
1/1 [==============================] - 0s 93ms/step
1/1 [==============================] - 0s 88ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 89ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 90ms/step
1/1 [==============================] - 0s 92ms/step
1/1 [==============================] - 0s 91ms/step

Final 160-image grid saved as: grid_final_160(2).png

def load_best_and_save_grid(best_save_dir, latent_dim=128, images_per_class=10, output_path="grid_best_160.png"):
    all_images = []

    for class_idx in range(16):
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(best_save_dir, f"best_generator_class_{class_idx}.h5")
        generator.load_weights(weight_path)

        noise = np.random.normal(0, 1, (images_per_class, latent_dim))
        generated = generator.predict(noise)
        generated = 0.5 * generated + 0.5

        for i in range(images_per_class):
            all_images.append(generated[i, :, :, 0])

    fig, axes = plt.subplots(16, 10, figsize=(10, 16))
    for idx, ax in enumerate(axes.flat):
        ax.imshow(all_images[idx], cmap='gray')
        ax.axis('off')
    plt.tight_layout()
    plt.suptitle("Best Generator — 160 Images", fontsize=16)
    plt.subplots_adjust(top=0.95)
    plt.savefig(output_path)
    plt.show()
    print(f"Best 160-image grid saved as: {output_path}")

def load_dcgan_models_from_weights(weight_dir, latent_dim=128):
    dcgan_models = {}
    for class_idx in range(16):
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(weight_dir, f"best_generator_class_{class_idx}.h5")
        if not os.path.exists(weight_path):  # Try loading final weights if best not found
            weight_path = os.path.join(weight_dir, f"final_generator_class_{class_idx}.h5")
        generator.load_weights(weight_path)

        # Wrap inside dummy DCGAN model structure
        dcgan = tf.keras.models.Model()
        dcgan.generator = generator
        dcgan_models[class_idx] = dcgan
    return dcgan_models

# For final weights
dcgan_models_final = load_dcgan_models_from_weights("16Models_saved_generators_600epochs(last)")

generate_word_from_models(dcgan_models_final, "DEEP LEANING")     # Final weights

Skipping ' ' — not trained.

generate_word_from_models(dcgan_models_final, "DEEP LEANING")     # Final weights

Skipping ' ' — not trained.

generate_word_from_models(dcgan_models_final, "ZAP")     # Final weights

generate_word_from_models(dcgan_models_final, "A BIG JAZZED FOX PLANT BOX QTIP")     # Final weights

Skipping ' ' — not trained.
Skipping ' ' — not trained.
Skipping ' ' — not trained.
Skipping ' ' — not trained.
Skipping ' ' — not trained.
Skipping ' ' — not trained.

generate_word_from_models(dcgan_models_final, "JING N DA")     # Final weights

Skipping ' ' — not trained.
Skipping ' ' — not trained.

generate_word_from_models(dcgan_models_final, "JING N DA")     # Final weights

Skipping ' ' — not trained.
Skipping ' ' — not trained.

import os
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras.layers import (Input, Dense, Reshape, Conv2DTranspose, BatchNormalization,
                                     Activation, Conv2D, LeakyReLU, Dropout, Flatten)
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.optimizers import Adam

# === Generator ===
def build_generator(latent_dim, channels=1):
    model = Sequential([
        Dense(256 * 7 * 7, input_dim=latent_dim),
        BatchNormalization(),
        LeakyReLU(0.2),
        Reshape((7, 7, 256)),

        Conv2DTranspose(128, kernel_size=4, strides=2, padding='same'),  # → 14x14
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2DTranspose(64, kernel_size=4, strides=2, padding='same'),   # → 28x28
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2DTranspose(32, kernel_size=3, strides=1, padding='same'),   # refinement
        BatchNormalization(),
        LeakyReLU(0.2),

        Conv2DTranspose(channels, kernel_size=2, strides=1, padding='same', activation='tanh')
    ])

    noise = Input(shape=(latent_dim,))
    img = model(noise)
    return Model(noise, img)

# === Discriminator ===
def build_discriminator(img_shape):
    model = Sequential([
        # Block 1
        Conv2D(64, kernel_size=3, strides=2, padding='same', input_shape=img_shape),
        LeakyReLU(0.2),
        Dropout(0.3),

        # Block 2
        Conv2D(128, kernel_size=3, strides=2, padding='same'),
        BatchNormalization(),
        LeakyReLU(0.2),
        Dropout(0.3),

        # Block 3
        Conv2D(128, kernel_size=3, strides=1, padding='same'),
        BatchNormalization(),
        LeakyReLU(0.2),

        Flatten(),
        Dense(64),
        LeakyReLU(0.2),
        Dense(1, activation='sigmoid')
    ])

    img = Input(shape=img_shape)
    validity = model(img)
    return Model(img, validity)

latent_dim = 128
img_shape = (28, 28, 1)
batch_size = 128
epochs = 500
num_classes = 16

class_datasets = prepare_class_datasets(images, mapped_labels, batch_size=batch_size, num_classes=num_classes)

# Build models
generator = build_generator(latent_dim, channels=1)
discriminator = build_discriminator(img_shape)

# Print summaries
print("=== Generator Summary ===")
generator.summary()

print("\n=== Discriminator Summary ===")
discriminator.summary()

=== Generator Summary ===
Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_39 (InputLayer)       [(None, 128)]             0         
                                                                 
 sequential_38 (Sequential)  (None, 28, 28, 1)         2343393   
                                                                 
=================================================================
Total params: 2,343,393
Trainable params: 2,317,857
Non-trainable params: 25,536
_________________________________________________________________

=== Discriminator Summary ===
Model: "model_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_40 (InputLayer)       [(None, 28, 28, 1)]       0         
                                                                 
 sequential_39 (Sequential)  (None, 1)                 624641    
                                                                 
=================================================================
Total params: 624,641
Trainable params: 624,129
Non-trainable params: 512
_________________________________________________________________

# === DCGAN Model Class ===
class DCGANModel(tf.keras.Model):
    def __init__(self, generator, discriminator, latent_dim):
        super().__init__()
        self.generator = generator
        self.discriminator = discriminator
        self.latent_dim = latent_dim
        self.loss_fn = tf.keras.losses.BinaryCrossentropy(from_logits=False)

    def compile(self, g_optimizer, d_optimizer):
        super().compile()
        self.g_optimizer = g_optimizer
        self.d_optimizer = d_optimizer

    def generator_loss(self, fake_output):
        return self.loss_fn(tf.ones_like(fake_output), fake_output)

    def discriminator_loss(self, real_output, fake_output):
        real_loss = self.loss_fn(tf.ones_like(real_output) * 0.9, real_output)  # label smoothing
        fake_loss = self.loss_fn(tf.zeros_like(fake_output), fake_output)
        return real_loss + fake_loss

    @tf.function
    def train_step(self, real_images):
        batch_size = tf.shape(real_images)[0]
        noise = tf.random.normal([batch_size, self.latent_dim])

        with tf.GradientTape() as disc_tape, tf.GradientTape() as gen_tape:
            generated_images = self.generator(noise, training=True)
            real_output = self.discriminator(real_images, training=True)
            fake_output = self.discriminator(generated_images, training=True)

            d_loss = self.discriminator_loss(real_output, fake_output)
            g_loss = self.generator_loss(fake_output)

        grads_d = disc_tape.gradient(d_loss, self.discriminator.trainable_variables)
        grads_g = gen_tape.gradient(g_loss, self.generator.trainable_variables)

        self.d_optimizer.apply_gradients(zip(grads_d, self.discriminator.trainable_variables))
        self.g_optimizer.apply_gradients(zip(grads_g, self.generator.trainable_variables))

        # === Compute discriminator accuracy ===
        real_preds = tf.cast(real_output > 0.5, tf.float32)
        fake_preds = tf.cast(fake_output < 0.5, tf.float32)

        real_accuracy = tf.reduce_mean(real_preds)
        fake_accuracy = tf.reduce_mean(fake_preds)
        d_accuracy = 0.5 * (real_accuracy + fake_accuracy)

        return {
            "d_loss": d_loss,
            "g_loss": g_loss,
            "d_accuracy": d_accuracy
        }

# === Callback to Save Best Generator ===
class SaveBestGenerator(tf.keras.callbacks.Callback):
    def __init__(self, save_path):
        super().__init__()
        self.save_path = save_path
        self.best_loss = float('inf')

    def on_epoch_end(self, epoch, logs=None):
        g_loss = logs.get('g_loss')
        if g_loss is not None and g_loss < self.best_loss:
            print(f"\nEpoch {epoch + 1}: Generator loss improved from {self.best_loss:.4f} to {g_loss:.4f}. Saving weights.")
            self.best_loss = g_loss
            self.model.generator.save_weights(self.save_path)

# === Callback to Print Images Every 250 Epochs ===
class PrintImagesEveryN(tf.keras.callbacks.Callback):
    def __init__(self, class_index, latent_dim, interval=250):
        super().__init__()
        self.class_index = class_index
        self.latent_dim = latent_dim
        self.interval = interval
        self.seed = tf.random.normal([10, latent_dim])

    def on_epoch_end(self, epoch, logs=None):
        if (epoch + 1) % self.interval == 0:
            generated_images = self.model.generator(self.seed, training=False)
            generated_images = 0.5 * generated_images + 0.5

            print(f"\nClass {self.class_index} — Epoch {epoch + 1} preview:")
            fig, axes = plt.subplots(1, 10, figsize=(20, 2))
            for i in range(10):
                ax = axes[i]
                ax.imshow(generated_images[i, :, :, 0], cmap='gray')
                ax.axis('off')
            plt.show()

# === Prepare Dataset Per Class ===
def prepare_class_datasets(images, labels, batch_size=128, num_classes=16):
    class_datasets = {}
    for c in range(num_classes):
        class_imgs = images[labels == c]
        dataset = tf.data.Dataset.from_tensor_slices(class_imgs) \
                               .shuffle(buffer_size=10000) \
                               .batch(batch_size) \
                               .prefetch(tf.data.AUTOTUNE)
        class_datasets[c] = dataset
    return class_datasets



class PrintAccuracy(tf.keras.callbacks.Callback):
    def on_epoch_end(self, epoch, logs=None):
        if logs and 'd_accuracy' in logs:
            print(f"Epoch {epoch + 1}: D Accuracy = {logs['d_accuracy']:.4f}")

# === Training ===

dcgan_models = {}
histories = {}

# Directory to save best weights
best_save_dir = "(2)16Models_saved_generators_epochs"
os.makedirs(best_save_dir, exist_ok=True)

for c in range(num_classes):
    print(f"Training DCGAN for class {c}...")

    generator = build_generator(latent_dim, channels=1)
    discriminator = build_discriminator(img_shape)

    dcgan = DCGANModel(generator, discriminator, latent_dim)
    dcgan.compile(
            g_optimizer=Adam(0.0002, beta_1=0.5),   # faster
            d_optimizer=Adam(0.00005, beta_1=0.5)   # slower
        )


    preview_callback = PrintImagesEveryN(class_index=c, latent_dim=latent_dim, interval=100)
    best_weight_path = os.path.join(best_save_dir, f"(2)best_generator_class_{c}.h5")
    save_best_callback = SaveBestGenerator(save_path=best_weight_path)
    accuracy_callback = PrintAccuracy()  

    history = dcgan.fit(
        class_datasets[c],
        epochs=epochs,
        callbacks=[preview_callback, save_best_callback, accuracy_callback]
    )

    dcgan_models[c] = dcgan
    histories[c] = history

# === Save final generator weights for all 16 classes ===
final_save_dir = "(2)16Models_saved_generators_epochs(last)"
os.makedirs(final_save_dir, exist_ok=True)

for class_idx, dcgan in dcgan_models.items():
    gen = dcgan.generator
    weight_path = os.path.join(final_save_dir, f"(2)final_generator_class_{class_idx}.h5")
    gen.save_weights(weight_path)
    print(f"Saved final generator weights for class {class_idx} to {weight_path}")

Training DCGAN for class 0...
Epoch 1/500
 5/27 [====>.........................] - ETA: 0s - d_loss: 1.3408 - g_loss: 0.9324 - d_accuracy: 0.6164WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0172s vs `on_train_batch_end` time: 0.0238s). Check your callbacks.
27/27 [==============================] - ETA: 0s - d_loss: 1.1475 - g_loss: 1.1655 - d_accuracy: 0.7217
Epoch 1: Generator loss improved from inf to 1.1482. Saving weights.
Epoch 1: D Accuracy = 0.7206
27/27 [==============================] - 2s 34ms/step - d_loss: 1.1496 - g_loss: 1.1648 - d_accuracy: 0.7217
Epoch 2/500
27/27 [==============================] - ETA: 0s - d_loss: 1.0839 - g_loss: 1.3087 - d_accuracy: 0.7734Epoch 2: D Accuracy = 0.8382
27/27 [==============================] - 1s 32ms/step - d_loss: 1.0771 - g_loss: 1.3171 - d_accuracy: 0.7757
Epoch 3/500
27/27 [==============================] - ETA: 0s - d_loss: 0.7492 - g_loss: 1.8428 - d_accuracy: 0.9039Epoch 3: D Accuracy = 0.9706
27/27 [==============================] - 1s 32ms/step - d_loss: 0.7436 - g_loss: 1.8519 - d_accuracy: 0.9062
Epoch 4/500
27/27 [==============================] - ETA: 0s - d_loss: 0.5309 - g_loss: 2.7726 - d_accuracy: 0.9636Epoch 4: D Accuracy = 0.9853
27/27 [==============================] - 1s 32ms/step - d_loss: 0.5278 - g_loss: 2.7931 - d_accuracy: 0.9644
Epoch 5/500
27/27 [==============================] - ETA: 0s - d_loss: 0.4939 - g_loss: 3.4929 - d_accuracy: 0.9685
Epoch 5: Generator loss improved from 1.1482 to 0.4685. Saving weights.
Epoch 5: D Accuracy = 0.5882
27/27 [==============================] - 1s 34ms/step - d_loss: 0.5383 - g_loss: 3.3849 - d_accuracy: 0.9550
Epoch 6/500
27/27 [==============================] - ETA: 0s - d_loss: 1.5197 - g_loss: 1.1571 - d_accuracy: 0.5818Epoch 6: D Accuracy = 0.5294
27/27 [==============================] - 1s 33ms/step - d_loss: 1.5215 - g_loss: 1.1462 - d_accuracy: 0.5799
Epoch 7/500
27/27 [==============================] - ETA: 0s - d_loss: 1.5281 - g_loss: 0.9102 - d_accuracy: 0.5135Epoch 7: D Accuracy = 0.5368
27/27 [==============================] - 1s 32ms/step - d_loss: 1.5260 - g_loss: 0.9105 - d_accuracy: 0.5143
Epoch 8/500
27/27 [==============================] - ETA: 0s - d_loss: 1.4494 - g_loss: 0.8862 - d_accuracy: 0.5359Epoch 8: D Accuracy = 0.5441
27/27 [==============================] - 1s 32ms/step - d_loss: 1.4480 - g_loss: 0.8864 - d_accuracy: 0.5362
Epoch 9/500
27/27 [==============================] - ETA: 0s - d_loss: 1.4220 - g_loss: 0.8799 - d_accuracy: 0.5355Epoch 9: D Accuracy = 0.5294
27/27 [==============================] - 1s 32ms/step - d_loss: 1.4233 - g_loss: 0.8780 - d_accuracy: 0.5353
Epoch 10/500
27/27 [==============================] - ETA: 0s - d_loss: 1.4242 - g_loss: 0.8541 - d_accuracy: 0.5316Epoch 10: D Accuracy = 0.5294
27/27 [==============================] - 1s 33ms/step - d_loss: 1.4228 - g_loss: 0.8549 - d_accuracy: 0.5315
Epoch 11/500
27/27 [==============================] - ETA: 0s - d_loss: 1.4012 - g_loss: 0.8471 - d_accuracy: 0.5314Epoch 11: D Accuracy = 0.4559
27/27 [==============================] - 1s 33ms/step - d_loss: 1.4026 - g_loss: 0.8476 - d_accuracy: 0.5287
Epoch 12/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3859 - g_loss: 0.8426 - d_accuracy: 0.5354Epoch 12: D Accuracy = 0.5662
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3852 - g_loss: 0.8436 - d_accuracy: 0.5365
Epoch 13/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3697 - g_loss: 0.8475 - d_accuracy: 0.5502Epoch 13: D Accuracy = 0.5588
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3692 - g_loss: 0.8475 - d_accuracy: 0.5505
Epoch 14/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3664 - g_loss: 0.8467 - d_accuracy: 0.5511Epoch 14: D Accuracy = 0.5368
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3670 - g_loss: 0.8451 - d_accuracy: 0.5506
Epoch 15/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3624 - g_loss: 0.8447 - d_accuracy: 0.5543Epoch 15: D Accuracy = 0.5515
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3622 - g_loss: 0.8455 - d_accuracy: 0.5542
Epoch 16/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3601 - g_loss: 0.8459 - d_accuracy: 0.5459Epoch 16: D Accuracy = 0.5882
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3589 - g_loss: 0.8466 - d_accuracy: 0.5475
Epoch 17/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3447 - g_loss: 0.8533 - d_accuracy: 0.5725Epoch 17: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3434 - g_loss: 0.8545 - d_accuracy: 0.5744
Epoch 18/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3434 - g_loss: 0.8569 - d_accuracy: 0.5661Epoch 18: D Accuracy = 0.5735
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3432 - g_loss: 0.8568 - d_accuracy: 0.5664
Epoch 19/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3431 - g_loss: 0.8544 - d_accuracy: 0.5744Epoch 19: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3434 - g_loss: 0.8535 - d_accuracy: 0.5759
Epoch 20/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3226 - g_loss: 0.8691 - d_accuracy: 0.5905Epoch 20: D Accuracy = 0.5294
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3240 - g_loss: 0.8687 - d_accuracy: 0.5883
Epoch 21/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3225 - g_loss: 0.8755 - d_accuracy: 0.5900Epoch 21: D Accuracy = 0.5662
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3224 - g_loss: 0.8771 - d_accuracy: 0.5891
Epoch 22/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3154 - g_loss: 0.8785 - d_accuracy: 0.6010Epoch 22: D Accuracy = 0.5441
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3162 - g_loss: 0.8788 - d_accuracy: 0.5990
Epoch 23/500
27/27 [==============================] - ETA: 0s - d_loss: 1.3022 - g_loss: 0.8900 - d_accuracy: 0.6173Epoch 23: D Accuracy = 0.6985
27/27 [==============================] - 1s 33ms/step - d_loss: 1.3003 - g_loss: 0.8906 - d_accuracy: 0.6202
Epoch 24/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2844 - g_loss: 0.9125 - d_accuracy: 0.6343Epoch 24: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2834 - g_loss: 0.9133 - d_accuracy: 0.6353
Epoch 25/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2806 - g_loss: 0.9184 - d_accuracy: 0.6267Epoch 25: D Accuracy = 0.5662
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2821 - g_loss: 0.9168 - d_accuracy: 0.6246
Epoch 26/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2716 - g_loss: 0.9271 - d_accuracy: 0.6416Epoch 26: D Accuracy = 0.5882
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2726 - g_loss: 0.9282 - d_accuracy: 0.6397
Epoch 27/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2515 - g_loss: 0.9478 - d_accuracy: 0.6590Epoch 27: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2519 - g_loss: 0.9468 - d_accuracy: 0.6599
Epoch 28/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2443 - g_loss: 0.9586 - d_accuracy: 0.6611Epoch 28: D Accuracy = 0.7279
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2422 - g_loss: 0.9615 - d_accuracy: 0.6635
Epoch 29/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2332 - g_loss: 0.9796 - d_accuracy: 0.6665Epoch 29: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2331 - g_loss: 0.9813 - d_accuracy: 0.6660
Epoch 30/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2194 - g_loss: 0.9976 - d_accuracy: 0.6794Epoch 30: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2194 - g_loss: 0.9992 - d_accuracy: 0.6791
Epoch 31/500

27/27 [==============================] - ETA: 0s - d_loss: 1.2181 - g_loss: 1.0009 - d_accuracy: 0.6770Epoch 31: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2188 - g_loss: 0.9992 - d_accuracy: 0.6765
Epoch 32/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2229 - g_loss: 1.0053 - d_accuracy: 0.6817Epoch 32: D Accuracy = 0.6765
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2245 - g_loss: 1.0048 - d_accuracy: 0.6816
Epoch 33/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2257 - g_loss: 1.0042 - d_accuracy: 0.6767Epoch 33: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2277 - g_loss: 1.0022 - d_accuracy: 0.6743
Epoch 34/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2480 - g_loss: 0.9911 - d_accuracy: 0.6503Epoch 34: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2468 - g_loss: 0.9934 - d_accuracy: 0.6518
Epoch 35/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2407 - g_loss: 1.0022 - d_accuracy: 0.6683Epoch 35: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2429 - g_loss: 1.0048 - d_accuracy: 0.6662
Epoch 36/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2422 - g_loss: 1.0003 - d_accuracy: 0.6548Epoch 36: D Accuracy = 0.5735
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2444 - g_loss: 1.0038 - d_accuracy: 0.6519
Epoch 37/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2315 - g_loss: 0.9987 - d_accuracy: 0.6679Epoch 37: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2309 - g_loss: 0.9994 - d_accuracy: 0.6677
Epoch 38/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2350 - g_loss: 1.0174 - d_accuracy: 0.6584Epoch 38: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2362 - g_loss: 1.0168 - d_accuracy: 0.6572
Epoch 39/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2393 - g_loss: 0.9970 - d_accuracy: 0.6634Epoch 39: D Accuracy = 0.6397
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2396 - g_loss: 0.9974 - d_accuracy: 0.6626
Epoch 40/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2455 - g_loss: 1.0023 - d_accuracy: 0.6522Epoch 40: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2453 - g_loss: 1.0054 - d_accuracy: 0.6507
Epoch 41/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2380 - g_loss: 1.0054 - d_accuracy: 0.6625Epoch 41: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2370 - g_loss: 1.0068 - d_accuracy: 0.6632
Epoch 42/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2356 - g_loss: 1.0109 - d_accuracy: 0.6572Epoch 42: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2352 - g_loss: 1.0106 - d_accuracy: 0.6576
Epoch 43/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2318 - g_loss: 1.0125 - d_accuracy: 0.6614Epoch 43: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2299 - g_loss: 1.0120 - d_accuracy: 0.6625
Epoch 44/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2430 - g_loss: 1.0034 - d_accuracy: 0.6586Epoch 44: D Accuracy = 0.6029
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2447 - g_loss: 1.0010 - d_accuracy: 0.6566
Epoch 45/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2588 - g_loss: 0.9933 - d_accuracy: 0.6497Epoch 45: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2557 - g_loss: 0.9949 - d_accuracy: 0.6522
Epoch 46/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2518 - g_loss: 0.9892 - d_accuracy: 0.6542Epoch 46: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2508 - g_loss: 0.9868 - d_accuracy: 0.6555
Epoch 47/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2480 - g_loss: 0.9958 - d_accuracy: 0.6610Epoch 47: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2497 - g_loss: 0.9921 - d_accuracy: 0.6597
Epoch 48/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2564 - g_loss: 0.9904 - d_accuracy: 0.6522Epoch 48: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2553 - g_loss: 0.9958 - d_accuracy: 0.6533
Epoch 49/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2602 - g_loss: 0.9857 - d_accuracy: 0.6457Epoch 49: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2623 - g_loss: 0.9860 - d_accuracy: 0.6445
Epoch 50/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2574 - g_loss: 0.9911 - d_accuracy: 0.6479Epoch 50: D Accuracy = 0.6765
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2562 - g_loss: 0.9937 - d_accuracy: 0.6489
Epoch 51/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2605 - g_loss: 0.9885 - d_accuracy: 0.6481Epoch 51: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2595 - g_loss: 0.9912 - d_accuracy: 0.6483
Epoch 52/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2671 - g_loss: 0.9828 - d_accuracy: 0.6427Epoch 52: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2670 - g_loss: 0.9879 - d_accuracy: 0.6420
Epoch 53/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2628 - g_loss: 0.9821 - d_accuracy: 0.6456Epoch 53: D Accuracy = 0.5956
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2647 - g_loss: 0.9806 - d_accuracy: 0.6438
Epoch 54/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2605 - g_loss: 0.9903 - d_accuracy: 0.6459Epoch 54: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2590 - g_loss: 0.9916 - d_accuracy: 0.6462
Epoch 55/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2660 - g_loss: 0.9784 - d_accuracy: 0.6442Epoch 55: D Accuracy = 0.7206
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2644 - g_loss: 0.9781 - d_accuracy: 0.6469
Epoch 56/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2539 - g_loss: 0.9920 - d_accuracy: 0.6526Epoch 56: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2512 - g_loss: 0.9949 - d_accuracy: 0.6545
Epoch 57/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2507 - g_loss: 0.9885 - d_accuracy: 0.6502Epoch 57: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2499 - g_loss: 0.9857 - d_accuracy: 0.6509
Epoch 58/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2558 - g_loss: 0.9874 - d_accuracy: 0.6460Epoch 58: D Accuracy = 0.5956
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2581 - g_loss: 0.9869 - d_accuracy: 0.6442
Epoch 59/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2445 - g_loss: 0.9921 - d_accuracy: 0.6556Epoch 59: D Accuracy = 0.6397
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2427 - g_loss: 0.9971 - d_accuracy: 0.6551
Epoch 60/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2525 - g_loss: 0.9886 - d_accuracy: 0.6508Epoch 60: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2530 - g_loss: 0.9897 - d_accuracy: 0.6510
Epoch 61/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2604 - g_loss: 0.9906 - d_accuracy: 0.6395Epoch 61: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2594 - g_loss: 0.9896 - d_accuracy: 0.6403
Epoch 62/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2500 - g_loss: 0.9914 - d_accuracy: 0.6535Epoch 62: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2499 - g_loss: 0.9897 - d_accuracy: 0.6548
Epoch 63/500

27/27 [==============================] - ETA: 0s - d_loss: 1.2491 - g_loss: 0.9964 - d_accuracy: 0.6572Epoch 63: D Accuracy = 0.6397
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2482 - g_loss: 0.9972 - d_accuracy: 0.6566
Epoch 64/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2560 - g_loss: 0.9928 - d_accuracy: 0.6480Epoch 64: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2565 - g_loss: 0.9914 - d_accuracy: 0.6472
Epoch 65/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2590 - g_loss: 0.9888 - d_accuracy: 0.6535Epoch 65: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2602 - g_loss: 0.9854 - d_accuracy: 0.6522
Epoch 66/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2644 - g_loss: 0.9887 - d_accuracy: 0.6417Epoch 66: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2631 - g_loss: 0.9944 - d_accuracy: 0.6403
Epoch 67/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2617 - g_loss: 0.9799 - d_accuracy: 0.6388Epoch 67: D Accuracy = 0.6471
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2629 - g_loss: 0.9768 - d_accuracy: 0.6391
Epoch 68/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2599 - g_loss: 0.9889 - d_accuracy: 0.6418Epoch 68: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2593 - g_loss: 0.9894 - d_accuracy: 0.6412
Epoch 69/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2475 - g_loss: 0.9973 - d_accuracy: 0.6612Epoch 69: D Accuracy = 0.6176
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2506 - g_loss: 0.9968 - d_accuracy: 0.6596
Epoch 70/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2609 - g_loss: 0.9804 - d_accuracy: 0.6443Epoch 70: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2609 - g_loss: 0.9833 - d_accuracy: 0.6447
Epoch 71/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2605 - g_loss: 0.9776 - d_accuracy: 0.6487Epoch 71: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2609 - g_loss: 0.9742 - d_accuracy: 0.6502
Epoch 72/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2573 - g_loss: 0.9892 - d_accuracy: 0.6520Epoch 72: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2552 - g_loss: 0.9888 - d_accuracy: 0.6539
Epoch 73/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2604 - g_loss: 0.9834 - d_accuracy: 0.6484Epoch 73: D Accuracy = 0.6471
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2615 - g_loss: 0.9777 - d_accuracy: 0.6483
Epoch 74/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2595 - g_loss: 0.9816 - d_accuracy: 0.6454Epoch 74: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2605 - g_loss: 0.9805 - d_accuracy: 0.6442
Epoch 75/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2721 - g_loss: 0.9710 - d_accuracy: 0.6409Epoch 75: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2740 - g_loss: 0.9731 - d_accuracy: 0.6399
Epoch 76/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2577 - g_loss: 0.9886 - d_accuracy: 0.6537Epoch 76: D Accuracy = 0.6029
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2593 - g_loss: 0.9902 - d_accuracy: 0.6519
Epoch 77/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2738 - g_loss: 0.9756 - d_accuracy: 0.6352Epoch 77: D Accuracy = 0.6618
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2744 - g_loss: 0.9739 - d_accuracy: 0.6361
Epoch 78/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2625 - g_loss: 0.9797 - d_accuracy: 0.6442Epoch 78: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2632 - g_loss: 0.9809 - d_accuracy: 0.6445
Epoch 79/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2701 - g_loss: 0.9747 - d_accuracy: 0.6403Epoch 79: D Accuracy = 0.6324
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2708 - g_loss: 0.9764 - d_accuracy: 0.6400
Epoch 80/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2600 - g_loss: 0.9794 - d_accuracy: 0.6491Epoch 80: D Accuracy = 0.6397
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2600 - g_loss: 0.9765 - d_accuracy: 0.6488
Epoch 81/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2643 - g_loss: 0.9865 - d_accuracy: 0.6447Epoch 81: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2645 - g_loss: 0.9919 - d_accuracy: 0.6456
Epoch 82/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2668 - g_loss: 0.9742 - d_accuracy: 0.6443Epoch 82: D Accuracy = 0.7059
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2650 - g_loss: 0.9734 - d_accuracy: 0.6465
Epoch 83/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2751 - g_loss: 0.9759 - d_accuracy: 0.6340Epoch 83: D Accuracy = 0.6544
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2737 - g_loss: 0.9791 - d_accuracy: 0.6348
Epoch 84/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2650 - g_loss: 0.9763 - d_accuracy: 0.6433Epoch 84: D Accuracy = 0.5809
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2667 - g_loss: 0.9734 - d_accuracy: 0.6411
Epoch 85/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2688 - g_loss: 0.9748 - d_accuracy: 0.6403Epoch 85: D Accuracy = 0.6471
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2690 - g_loss: 0.9747 - d_accuracy: 0.6405
Epoch 86/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2714 - g_loss: 0.9752 - d_accuracy: 0.6312Epoch 86: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2692 - g_loss: 0.9785 - d_accuracy: 0.6329
Epoch 87/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2807 - g_loss: 0.9731 - d_accuracy: 0.6313Epoch 87: D Accuracy = 0.7132
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2773 - g_loss: 0.9780 - d_accuracy: 0.6342
Epoch 88/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2808 - g_loss: 0.9710 - d_accuracy: 0.6270Epoch 88: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2817 - g_loss: 0.9689 - d_accuracy: 0.6272
Epoch 89/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2802 - g_loss: 0.9706 - d_accuracy: 0.6350Epoch 89: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2804 - g_loss: 0.9686 - d_accuracy: 0.6344
Epoch 90/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2826 - g_loss: 0.9675 - d_accuracy: 0.6291Epoch 90: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2829 - g_loss: 0.9709 - d_accuracy: 0.6289
Epoch 91/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2769 - g_loss: 0.9684 - d_accuracy: 0.6343Epoch 91: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2767 - g_loss: 0.9733 - d_accuracy: 0.6334
Epoch 92/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2727 - g_loss: 0.9726 - d_accuracy: 0.6323Epoch 92: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2713 - g_loss: 0.9732 - d_accuracy: 0.6336
Epoch 93/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2796 - g_loss: 0.9672 - d_accuracy: 0.6302Epoch 93: D Accuracy = 0.5074
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2832 - g_loss: 0.9683 - d_accuracy: 0.6258
Epoch 94/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2800 - g_loss: 0.9627 - d_accuracy: 0.6314Epoch 94: D Accuracy = 0.6250
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2788 - g_loss: 0.9612 - d_accuracy: 0.6311
Epoch 95/500

27/27 [==============================] - ETA: 0s - d_loss: 1.2772 - g_loss: 0.9697 - d_accuracy: 0.6335Epoch 95: D Accuracy = 0.5809
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2798 - g_loss: 0.9674 - d_accuracy: 0.6316
Epoch 96/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2813 - g_loss: 0.9591 - d_accuracy: 0.6306Epoch 96: D Accuracy = 0.5735
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2822 - g_loss: 0.9594 - d_accuracy: 0.6286
Epoch 97/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2762 - g_loss: 0.9691 - d_accuracy: 0.6392Epoch 97: D Accuracy = 0.6691
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2753 - g_loss: 0.9714 - d_accuracy: 0.6403
Epoch 98/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2833 - g_loss: 0.9706 - d_accuracy: 0.6219Epoch 98: D Accuracy = 0.5809
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2833 - g_loss: 0.9722 - d_accuracy: 0.6205
Epoch 99/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2766 - g_loss: 0.9661 - d_accuracy: 0.6319Epoch 99: D Accuracy = 0.6324
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2770 - g_loss: 0.9651 - d_accuracy: 0.6319
Epoch 100/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2795 - g_loss: 0.9704 - d_accuracy: 0.6342
Class 0 — Epoch 100 preview:

Epoch 100: D Accuracy = 0.5956
27/27 [==============================] - 1s 40ms/step - d_loss: 1.2809 - g_loss: 0.9724 - d_accuracy: 0.6328
Epoch 101/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2718 - g_loss: 0.9706 - d_accuracy: 0.6408Epoch 101: D Accuracy = 0.6912
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2702 - g_loss: 0.9696 - d_accuracy: 0.6426
Epoch 102/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2749 - g_loss: 0.9648 - d_accuracy: 0.6419Epoch 102: D Accuracy = 0.6838
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2748 - g_loss: 0.9614 - d_accuracy: 0.6434
Epoch 103/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2838 - g_loss: 0.9544 - d_accuracy: 0.6261Epoch 103: D Accuracy = 0.6544
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2844 - g_loss: 0.9464 - d_accuracy: 0.6271
Epoch 104/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2793 - g_loss: 0.9714 - d_accuracy: 0.6361Epoch 104: D Accuracy = 0.6618
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2778 - g_loss: 0.9753 - d_accuracy: 0.6370
Epoch 105/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2888 - g_loss: 0.9497 - d_accuracy: 0.6290Epoch 105: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2893 - g_loss: 0.9451 - d_accuracy: 0.6296
Epoch 106/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2746 - g_loss: 0.9672 - d_accuracy: 0.6256Epoch 106: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2747 - g_loss: 0.9656 - d_accuracy: 0.6246
Epoch 107/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2876 - g_loss: 0.9541 - d_accuracy: 0.6219Epoch 107: D Accuracy = 0.5882
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2899 - g_loss: 0.9527 - d_accuracy: 0.6207
Epoch 108/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2845 - g_loss: 0.9581 - d_accuracy: 0.6262Epoch 108: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2827 - g_loss: 0.9583 - d_accuracy: 0.6280
Epoch 109/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2923 - g_loss: 0.9526 - d_accuracy: 0.6253Epoch 109: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2930 - g_loss: 0.9535 - d_accuracy: 0.6248
Epoch 110/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2752 - g_loss: 0.9624 - d_accuracy: 0.6363Epoch 110: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2765 - g_loss: 0.9584 - d_accuracy: 0.6354
Epoch 111/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2857 - g_loss: 0.9615 - d_accuracy: 0.6234Epoch 111: D Accuracy = 0.5662
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2850 - g_loss: 0.9646 - d_accuracy: 0.6214
Epoch 112/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2783 - g_loss: 0.9604 - d_accuracy: 0.6316Epoch 112: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2792 - g_loss: 0.9587 - d_accuracy: 0.6317
Epoch 113/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2762 - g_loss: 0.9628 - d_accuracy: 0.6305Epoch 113: D Accuracy = 0.5441
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2799 - g_loss: 0.9587 - d_accuracy: 0.6275
Epoch 114/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2854 - g_loss: 0.9571 - d_accuracy: 0.6265Epoch 114: D Accuracy = 0.5368
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2898 - g_loss: 0.9548 - d_accuracy: 0.6233
Epoch 115/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2845 - g_loss: 0.9565 - d_accuracy: 0.6295Epoch 115: D Accuracy = 0.6324
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2842 - g_loss: 0.9572 - d_accuracy: 0.6296
Epoch 116/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2806 - g_loss: 0.9542 - d_accuracy: 0.6268Epoch 116: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2817 - g_loss: 0.9522 - d_accuracy: 0.6259
Epoch 117/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2795 - g_loss: 0.9600 - d_accuracy: 0.6262Epoch 117: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2799 - g_loss: 0.9616 - d_accuracy: 0.6251
Epoch 118/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2894 - g_loss: 0.9481 - d_accuracy: 0.6265Epoch 118: D Accuracy = 0.6691
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2879 - g_loss: 0.9475 - d_accuracy: 0.6280
Epoch 119/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2813 - g_loss: 0.9646 - d_accuracy: 0.6277Epoch 119: D Accuracy = 0.6471
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2790 - g_loss: 0.9711 - d_accuracy: 0.6284
Epoch 120/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2731 - g_loss: 0.9595 - d_accuracy: 0.6438Epoch 120: D Accuracy = 0.6397
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2725 - g_loss: 0.9593 - d_accuracy: 0.6436
Epoch 121/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2704 - g_loss: 0.9693 - d_accuracy: 0.6382Epoch 121: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2716 - g_loss: 0.9669 - d_accuracy: 0.6374
Epoch 122/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2683 - g_loss: 0.9712 - d_accuracy: 0.6403Epoch 122: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2691 - g_loss: 0.9705 - d_accuracy: 0.6387
Epoch 123/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2718 - g_loss: 0.9671 - d_accuracy: 0.6321Epoch 123: D Accuracy = 0.6103
27/27 [==============================] - 1s 33ms/step - d_loss: 1.2720 - g_loss: 0.9671 - d_accuracy: 0.6313
Epoch 124/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2836 - g_loss: 0.9668 - d_accuracy: 0.6247Epoch 124: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2838 - g_loss: 0.9739 - d_accuracy: 0.6242
Epoch 125/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2883 - g_loss: 0.9514 - d_accuracy: 0.6275Epoch 125: D Accuracy = 0.5662
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2891 - g_loss: 0.9479 - d_accuracy: 0.6253
Epoch 126/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2798 - g_loss: 0.9670 - d_accuracy: 0.6281Epoch 126: D Accuracy = 0.5809
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2802 - g_loss: 0.9702 - d_accuracy: 0.6265
Epoch 127/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2718 - g_loss: 0.9641 - d_accuracy: 0.6334Epoch 127: D Accuracy = 0.5735
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2738 - g_loss: 0.9627 - d_accuracy: 0.6312
Epoch 128/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2742 - g_loss: 0.9601 - d_accuracy: 0.6303Epoch 128: D Accuracy = 0.6691
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2732 - g_loss: 0.9588 - d_accuracy: 0.6316
Epoch 129/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2777 - g_loss: 0.9642 - d_accuracy: 0.6340Epoch 129: D Accuracy = 0.6838
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2764 - g_loss: 0.9622 - d_accuracy: 0.6358
Epoch 130/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2841 - g_loss: 0.9626 - d_accuracy: 0.6292Epoch 130: D Accuracy = 0.5809
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2871 - g_loss: 0.9616 - d_accuracy: 0.6274
Epoch 131/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2609 - g_loss: 0.9769 - d_accuracy: 0.6384Epoch 131: D Accuracy = 0.6985
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2583 - g_loss: 0.9774 - d_accuracy: 0.6406
Epoch 132/500

27/27 [==============================] - ETA: 0s - d_loss: 1.2672 - g_loss: 0.9724 - d_accuracy: 0.6334Epoch 132: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2679 - g_loss: 0.9755 - d_accuracy: 0.6326
Epoch 133/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2775 - g_loss: 0.9588 - d_accuracy: 0.6299Epoch 133: D Accuracy = 0.5515
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2807 - g_loss: 0.9569 - d_accuracy: 0.6271
Epoch 134/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2758 - g_loss: 0.9727 - d_accuracy: 0.6257Epoch 134: D Accuracy = 0.6176
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2745 - g_loss: 0.9743 - d_accuracy: 0.6255
Epoch 135/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2801 - g_loss: 0.9541 - d_accuracy: 0.6254Epoch 135: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2794 - g_loss: 0.9537 - d_accuracy: 0.6259
Epoch 136/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2822 - g_loss: 0.9538 - d_accuracy: 0.6231Epoch 136: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2836 - g_loss: 0.9493 - d_accuracy: 0.6229
Epoch 137/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2833 - g_loss: 0.9634 - d_accuracy: 0.6294Epoch 137: D Accuracy = 0.5441
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2851 - g_loss: 0.9633 - d_accuracy: 0.6263
Epoch 138/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2637 - g_loss: 0.9675 - d_accuracy: 0.6405Epoch 138: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2639 - g_loss: 0.9641 - d_accuracy: 0.6415
Epoch 139/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2896 - g_loss: 0.9608 - d_accuracy: 0.6164Epoch 139: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2905 - g_loss: 0.9590 - d_accuracy: 0.6156
Epoch 140/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2759 - g_loss: 0.9595 - d_accuracy: 0.6313Epoch 140: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2766 - g_loss: 0.9545 - d_accuracy: 0.6319
Epoch 141/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2750 - g_loss: 0.9638 - d_accuracy: 0.6331Epoch 141: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2739 - g_loss: 0.9605 - d_accuracy: 0.6347
Epoch 142/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2799 - g_loss: 0.9659 - d_accuracy: 0.6327Epoch 142: D Accuracy = 0.6765
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2788 - g_loss: 0.9694 - d_accuracy: 0.6343
Epoch 143/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2713 - g_loss: 0.9649 - d_accuracy: 0.6346Epoch 143: D Accuracy = 0.6176
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2717 - g_loss: 0.9683 - d_accuracy: 0.6340
Epoch 144/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2687 - g_loss: 0.9673 - d_accuracy: 0.6335Epoch 144: D Accuracy = 0.5221
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2718 - g_loss: 0.9619 - d_accuracy: 0.6295
Epoch 145/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2879 - g_loss: 0.9489 - d_accuracy: 0.6188Epoch 145: D Accuracy = 0.6691
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2886 - g_loss: 0.9447 - d_accuracy: 0.6206
Epoch 146/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2736 - g_loss: 0.9636 - d_accuracy: 0.6335Epoch 146: D Accuracy = 0.5735
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2745 - g_loss: 0.9669 - d_accuracy: 0.6314
Epoch 147/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2762 - g_loss: 0.9585 - d_accuracy: 0.6365Epoch 147: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2762 - g_loss: 0.9595 - d_accuracy: 0.6369
Epoch 148/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2712 - g_loss: 0.9683 - d_accuracy: 0.6364Epoch 148: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2708 - g_loss: 0.9685 - d_accuracy: 0.6360
Epoch 149/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2764 - g_loss: 0.9664 - d_accuracy: 0.6334Epoch 149: D Accuracy = 0.5662
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2795 - g_loss: 0.9679 - d_accuracy: 0.6310
Epoch 150/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2754 - g_loss: 0.9602 - d_accuracy: 0.6308Epoch 150: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2786 - g_loss: 0.9541 - d_accuracy: 0.6306
Epoch 151/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2659 - g_loss: 0.9722 - d_accuracy: 0.6367Epoch 151: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2666 - g_loss: 0.9723 - d_accuracy: 0.6361
Epoch 152/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2794 - g_loss: 0.9611 - d_accuracy: 0.6267Epoch 152: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2787 - g_loss: 0.9592 - d_accuracy: 0.6274
Epoch 153/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2729 - g_loss: 0.9668 - d_accuracy: 0.6304Epoch 153: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2749 - g_loss: 0.9628 - d_accuracy: 0.6292
Epoch 154/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2694 - g_loss: 0.9663 - d_accuracy: 0.6365Epoch 154: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2705 - g_loss: 0.9654 - d_accuracy: 0.6350
Epoch 155/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2664 - g_loss: 0.9694 - d_accuracy: 0.6361Epoch 155: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2660 - g_loss: 0.9656 - d_accuracy: 0.6360
Epoch 156/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2885 - g_loss: 0.9581 - d_accuracy: 0.6229Epoch 156: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2913 - g_loss: 0.9516 - d_accuracy: 0.6222
Epoch 157/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2721 - g_loss: 0.9704 - d_accuracy: 0.6322Epoch 157: D Accuracy = 0.5882
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2731 - g_loss: 0.9686 - d_accuracy: 0.6306
Epoch 158/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2745 - g_loss: 0.9611 - d_accuracy: 0.6283Epoch 158: D Accuracy = 0.5735
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2759 - g_loss: 0.9589 - d_accuracy: 0.6263
Epoch 159/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2759 - g_loss: 0.9601 - d_accuracy: 0.6321Epoch 159: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2766 - g_loss: 0.9600 - d_accuracy: 0.6321
Epoch 160/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2710 - g_loss: 0.9612 - d_accuracy: 0.6319Epoch 160: D Accuracy = 0.5735
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2728 - g_loss: 0.9580 - d_accuracy: 0.6298
Epoch 161/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2703 - g_loss: 0.9638 - d_accuracy: 0.6389Epoch 161: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2692 - g_loss: 0.9632 - d_accuracy: 0.6389
Epoch 162/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2723 - g_loss: 0.9675 - d_accuracy: 0.6323Epoch 162: D Accuracy = 0.5882
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2735 - g_loss: 0.9717 - d_accuracy: 0.6307
Epoch 163/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2715 - g_loss: 0.9624 - d_accuracy: 0.6360Epoch 163: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2703 - g_loss: 0.9638 - d_accuracy: 0.6358

Epoch 164/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2685 - g_loss: 0.9632 - d_accuracy: 0.6306Epoch 164: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2685 - g_loss: 0.9598 - d_accuracy: 0.6309
Epoch 165/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2635 - g_loss: 0.9789 - d_accuracy: 0.6376Epoch 165: D Accuracy = 0.5662
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2657 - g_loss: 0.9764 - d_accuracy: 0.6350
Epoch 166/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2629 - g_loss: 0.9703 - d_accuracy: 0.6474Epoch 166: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2623 - g_loss: 0.9672 - d_accuracy: 0.6487
Epoch 167/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2720 - g_loss: 0.9704 - d_accuracy: 0.6337Epoch 167: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2721 - g_loss: 0.9662 - d_accuracy: 0.6336
Epoch 168/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2777 - g_loss: 0.9633 - d_accuracy: 0.6312Epoch 168: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2791 - g_loss: 0.9561 - d_accuracy: 0.6310
Epoch 169/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2617 - g_loss: 0.9779 - d_accuracy: 0.6404Epoch 169: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2603 - g_loss: 0.9823 - d_accuracy: 0.6414
Epoch 170/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2725 - g_loss: 0.9636 - d_accuracy: 0.6412Epoch 170: D Accuracy = 0.5441
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2750 - g_loss: 0.9592 - d_accuracy: 0.6378
Epoch 171/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2788 - g_loss: 0.9709 - d_accuracy: 0.6313Epoch 171: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2791 - g_loss: 0.9742 - d_accuracy: 0.6305
Epoch 172/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2756 - g_loss: 0.9576 - d_accuracy: 0.6268Epoch 172: D Accuracy = 0.5956
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2777 - g_loss: 0.9549 - d_accuracy: 0.6257
Epoch 173/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2732 - g_loss: 0.9682 - d_accuracy: 0.6344Epoch 173: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2724 - g_loss: 0.9683 - d_accuracy: 0.6341
Epoch 174/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2752 - g_loss: 0.9613 - d_accuracy: 0.6322Epoch 174: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2756 - g_loss: 0.9611 - d_accuracy: 0.6325
Epoch 175/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2616 - g_loss: 0.9742 - d_accuracy: 0.6451Epoch 175: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2614 - g_loss: 0.9746 - d_accuracy: 0.6446
Epoch 176/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2600 - g_loss: 0.9802 - d_accuracy: 0.6429Epoch 176: D Accuracy = 0.6618
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2600 - g_loss: 0.9815 - d_accuracy: 0.6435
Epoch 177/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2676 - g_loss: 0.9707 - d_accuracy: 0.6337Epoch 177: D Accuracy = 0.5588
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2696 - g_loss: 0.9691 - d_accuracy: 0.6310
Epoch 178/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2696 - g_loss: 0.9632 - d_accuracy: 0.6436Epoch 178: D Accuracy = 0.6985
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2687 - g_loss: 0.9613 - d_accuracy: 0.6456
Epoch 179/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2685 - g_loss: 0.9758 - d_accuracy: 0.6328Epoch 179: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2684 - g_loss: 0.9795 - d_accuracy: 0.6325
Epoch 180/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2792 - g_loss: 0.9606 - d_accuracy: 0.6305Epoch 180: D Accuracy = 0.7059
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2779 - g_loss: 0.9633 - d_accuracy: 0.6331
Epoch 181/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2591 - g_loss: 0.9747 - d_accuracy: 0.6445Epoch 181: D Accuracy = 0.7132
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2562 - g_loss: 0.9778 - d_accuracy: 0.6469
Epoch 182/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2682 - g_loss: 0.9732 - d_accuracy: 0.6367Epoch 182: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2678 - g_loss: 0.9751 - d_accuracy: 0.6365
Epoch 183/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2623 - g_loss: 0.9779 - d_accuracy: 0.6454Epoch 183: D Accuracy = 0.6912
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2606 - g_loss: 0.9768 - d_accuracy: 0.6470
Epoch 184/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2761 - g_loss: 0.9704 - d_accuracy: 0.6292Epoch 184: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2749 - g_loss: 0.9683 - d_accuracy: 0.6312
Epoch 185/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2580 - g_loss: 0.9755 - d_accuracy: 0.6443Epoch 185: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2574 - g_loss: 0.9780 - d_accuracy: 0.6428
Epoch 186/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2678 - g_loss: 0.9717 - d_accuracy: 0.6307Epoch 186: D Accuracy = 0.6618
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2660 - g_loss: 0.9712 - d_accuracy: 0.6318
Epoch 187/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2719 - g_loss: 0.9673 - d_accuracy: 0.6438Epoch 187: D Accuracy = 0.7500
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2685 - g_loss: 0.9714 - d_accuracy: 0.6476
Epoch 188/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2648 - g_loss: 0.9690 - d_accuracy: 0.6399Epoch 188: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2643 - g_loss: 0.9686 - d_accuracy: 0.6401
Epoch 189/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2569 - g_loss: 0.9787 - d_accuracy: 0.6421Epoch 189: D Accuracy = 0.5515
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2585 - g_loss: 0.9783 - d_accuracy: 0.6389
Epoch 190/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2509 - g_loss: 0.9844 - d_accuracy: 0.6481Epoch 190: D Accuracy = 0.7206
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2509 - g_loss: 0.9810 - d_accuracy: 0.6507
Epoch 191/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2618 - g_loss: 0.9813 - d_accuracy: 0.6397Epoch 191: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2623 - g_loss: 0.9813 - d_accuracy: 0.6395
Epoch 192/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2703 - g_loss: 0.9739 - d_accuracy: 0.6346Epoch 192: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2691 - g_loss: 0.9732 - d_accuracy: 0.6363
Epoch 193/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2656 - g_loss: 0.9727 - d_accuracy: 0.6408Epoch 193: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2676 - g_loss: 0.9687 - d_accuracy: 0.6395
Epoch 194/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2588 - g_loss: 0.9814 - d_accuracy: 0.6396Epoch 194: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2594 - g_loss: 0.9797 - d_accuracy: 0.6396
Epoch 195/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2533 - g_loss: 0.9837 - d_accuracy: 0.6452Epoch 195: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2541 - g_loss: 0.9843 - d_accuracy: 0.6448

Epoch 196/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2525 - g_loss: 0.9855 - d_accuracy: 0.6454Epoch 196: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2523 - g_loss: 0.9856 - d_accuracy: 0.6447
Epoch 197/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2644 - g_loss: 0.9763 - d_accuracy: 0.6358Epoch 197: D Accuracy = 0.6544
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2640 - g_loss: 0.9768 - d_accuracy: 0.6364
Epoch 198/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2512 - g_loss: 0.9854 - d_accuracy: 0.6469Epoch 198: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2524 - g_loss: 0.9836 - d_accuracy: 0.6458
Epoch 199/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2441 - g_loss: 0.9972 - d_accuracy: 0.6499Epoch 199: D Accuracy = 0.6985
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2423 - g_loss: 1.0011 - d_accuracy: 0.6516
Epoch 200/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2674 - g_loss: 0.9766 - d_accuracy: 0.6384
Class 0 — Epoch 200 preview:

Epoch 200: D Accuracy = 0.6471
27/27 [==============================] - 1s 40ms/step - d_loss: 1.2669 - g_loss: 0.9800 - d_accuracy: 0.6387
Epoch 201/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2664 - g_loss: 0.9714 - d_accuracy: 0.6385Epoch 201: D Accuracy = 0.6250
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2682 - g_loss: 0.9647 - d_accuracy: 0.6380
Epoch 202/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2565 - g_loss: 0.9776 - d_accuracy: 0.6436Epoch 202: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2571 - g_loss: 0.9723 - d_accuracy: 0.6435
Epoch 203/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2529 - g_loss: 0.9915 - d_accuracy: 0.6460Epoch 203: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2532 - g_loss: 0.9917 - d_accuracy: 0.6455
Epoch 204/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2537 - g_loss: 0.9816 - d_accuracy: 0.6464Epoch 204: D Accuracy = 0.6765
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2524 - g_loss: 0.9827 - d_accuracy: 0.6475
Epoch 205/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2496 - g_loss: 0.9883 - d_accuracy: 0.6523Epoch 205: D Accuracy = 0.6912
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2485 - g_loss: 0.9916 - d_accuracy: 0.6537
Epoch 206/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2539 - g_loss: 0.9812 - d_accuracy: 0.6500Epoch 206: D Accuracy = 0.6324
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2552 - g_loss: 0.9783 - d_accuracy: 0.6494
Epoch 207/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2603 - g_loss: 0.9822 - d_accuracy: 0.6415Epoch 207: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2605 - g_loss: 0.9850 - d_accuracy: 0.6407
Epoch 208/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2508 - g_loss: 0.9864 - d_accuracy: 0.6472Epoch 208: D Accuracy = 0.5441
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2537 - g_loss: 0.9875 - d_accuracy: 0.6435
Epoch 209/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2490 - g_loss: 0.9845 - d_accuracy: 0.6600Epoch 209: D Accuracy = 0.7353
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2459 - g_loss: 0.9850 - d_accuracy: 0.6627
Epoch 210/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2551 - g_loss: 0.9849 - d_accuracy: 0.6444Epoch 210: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2538 - g_loss: 0.9856 - d_accuracy: 0.6458
Epoch 211/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2551 - g_loss: 0.9837 - d_accuracy: 0.6411Epoch 211: D Accuracy = 0.6103
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2578 - g_loss: 0.9773 - d_accuracy: 0.6400
Epoch 212/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2583 - g_loss: 0.9830 - d_accuracy: 0.6443Epoch 212: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2577 - g_loss: 0.9847 - d_accuracy: 0.6457
Epoch 213/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2424 - g_loss: 0.9912 - d_accuracy: 0.6492Epoch 213: D Accuracy = 0.6618
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2421 - g_loss: 0.9883 - d_accuracy: 0.6497
Epoch 214/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2525 - g_loss: 0.9877 - d_accuracy: 0.6515Epoch 214: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2512 - g_loss: 0.9892 - d_accuracy: 0.6526
Epoch 215/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2512 - g_loss: 0.9804 - d_accuracy: 0.6477Epoch 215: D Accuracy = 0.7059
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2487 - g_loss: 0.9785 - d_accuracy: 0.6498
Epoch 216/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2545 - g_loss: 0.9862 - d_accuracy: 0.6470Epoch 216: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2516 - g_loss: 0.9932 - d_accuracy: 0.6478
Epoch 217/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2561 - g_loss: 0.9780 - d_accuracy: 0.6423Epoch 217: D Accuracy = 0.5956
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2579 - g_loss: 0.9763 - d_accuracy: 0.6406
Epoch 218/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2636 - g_loss: 0.9844 - d_accuracy: 0.6390Epoch 218: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2650 - g_loss: 0.9872 - d_accuracy: 0.6390
Epoch 219/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2499 - g_loss: 0.9837 - d_accuracy: 0.6479Epoch 219: D Accuracy = 0.6838
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2469 - g_loss: 0.9849 - d_accuracy: 0.6492
Epoch 220/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2567 - g_loss: 0.9889 - d_accuracy: 0.6417Epoch 220: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2561 - g_loss: 0.9930 - d_accuracy: 0.6408
Epoch 221/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2517 - g_loss: 0.9806 - d_accuracy: 0.6480Epoch 221: D Accuracy = 0.6397
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2524 - g_loss: 0.9816 - d_accuracy: 0.6477
Epoch 222/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2462 - g_loss: 0.9858 - d_accuracy: 0.6490Epoch 222: D Accuracy = 0.6471
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2439 - g_loss: 0.9908 - d_accuracy: 0.6489
Epoch 223/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2488 - g_loss: 0.9873 - d_accuracy: 0.6500Epoch 223: D Accuracy = 0.7500
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2459 - g_loss: 0.9889 - d_accuracy: 0.6536
Epoch 224/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2522 - g_loss: 0.9945 - d_accuracy: 0.6452Epoch 224: D Accuracy = 0.6029
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2531 - g_loss: 1.0021 - d_accuracy: 0.6437
Epoch 225/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2392 - g_loss: 0.9869 - d_accuracy: 0.6564Epoch 225: D Accuracy = 0.6765
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2396 - g_loss: 0.9832 - d_accuracy: 0.6571
Epoch 226/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2475 - g_loss: 0.9899 - d_accuracy: 0.6560Epoch 226: D Accuracy = 0.6691
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2463 - g_loss: 0.9882 - d_accuracy: 0.6565
Epoch 227/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2454 - g_loss: 0.9857 - d_accuracy: 0.6492Epoch 227: D Accuracy = 0.7353
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2423 - g_loss: 0.9858 - d_accuracy: 0.6523
Epoch 228/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2441 - g_loss: 0.9968 - d_accuracy: 0.6509Epoch 228: D Accuracy = 0.5809
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2452 - g_loss: 0.9968 - d_accuracy: 0.6484
Epoch 229/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2553 - g_loss: 0.9882 - d_accuracy: 0.6471Epoch 229: D Accuracy = 0.7721
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2513 - g_loss: 0.9897 - d_accuracy: 0.6515
Epoch 230/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2419 - g_loss: 0.9973 - d_accuracy: 0.6523Epoch 230: D Accuracy = 0.6397
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2419 - g_loss: 1.0026 - d_accuracy: 0.6519
Epoch 231/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2439 - g_loss: 0.9937 - d_accuracy: 0.6563Epoch 231: D Accuracy = 0.6618
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2431 - g_loss: 0.9956 - d_accuracy: 0.6565
Epoch 232/500

27/27 [==============================] - ETA: 0s - d_loss: 1.2576 - g_loss: 0.9829 - d_accuracy: 0.6443Epoch 232: D Accuracy = 0.6691
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2576 - g_loss: 0.9813 - d_accuracy: 0.6452
Epoch 233/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2487 - g_loss: 0.9878 - d_accuracy: 0.6528Epoch 233: D Accuracy = 0.6765
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2483 - g_loss: 0.9841 - d_accuracy: 0.6536
Epoch 234/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2512 - g_loss: 0.9872 - d_accuracy: 0.6481Epoch 234: D Accuracy = 0.5662
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2550 - g_loss: 0.9817 - d_accuracy: 0.6452
Epoch 235/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2515 - g_loss: 0.9957 - d_accuracy: 0.6508Epoch 235: D Accuracy = 0.6544
27/27 [==============================] - 1s 37ms/step - d_loss: 1.2501 - g_loss: 1.0034 - d_accuracy: 0.6510
Epoch 236/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2478 - g_loss: 0.9843 - d_accuracy: 0.6506Epoch 236: D Accuracy = 0.6471
27/27 [==============================] - 1s 36ms/step - d_loss: 1.2476 - g_loss: 0.9846 - d_accuracy: 0.6504
Epoch 237/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2414 - g_loss: 0.9961 - d_accuracy: 0.6526Epoch 237: D Accuracy = 0.6324
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2418 - g_loss: 0.9992 - d_accuracy: 0.6519
Epoch 238/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2485 - g_loss: 0.9907 - d_accuracy: 0.6454Epoch 238: D Accuracy = 0.6176
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2491 - g_loss: 0.9906 - d_accuracy: 0.6444
Epoch 239/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2486 - g_loss: 0.9846 - d_accuracy: 0.6510Epoch 239: D Accuracy = 0.6544
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2482 - g_loss: 0.9808 - d_accuracy: 0.6511
Epoch 240/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2455 - g_loss: 0.9986 - d_accuracy: 0.6511Epoch 240: D Accuracy = 0.5809
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2479 - g_loss: 1.0037 - d_accuracy: 0.6486
Epoch 241/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2419 - g_loss: 0.9911 - d_accuracy: 0.6537Epoch 241: D Accuracy = 0.5882
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2450 - g_loss: 0.9881 - d_accuracy: 0.6514
Epoch 242/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2482 - g_loss: 0.9928 - d_accuracy: 0.6541Epoch 242: D Accuracy = 0.6691
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2474 - g_loss: 0.9905 - d_accuracy: 0.6547
Epoch 243/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2501 - g_loss: 0.9875 - d_accuracy: 0.6553Epoch 243: D Accuracy = 0.5882
27/27 [==============================] - 1s 36ms/step - d_loss: 1.2516 - g_loss: 0.9836 - d_accuracy: 0.6529
Epoch 244/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2366 - g_loss: 0.9976 - d_accuracy: 0.6574Epoch 244: D Accuracy = 0.5809
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2398 - g_loss: 0.9965 - d_accuracy: 0.6546
Epoch 245/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2626 - g_loss: 0.9763 - d_accuracy: 0.6400Epoch 245: D Accuracy = 0.5882
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2645 - g_loss: 0.9761 - d_accuracy: 0.6381
Epoch 246/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2579 - g_loss: 0.9850 - d_accuracy: 0.6364Epoch 246: D Accuracy = 0.5662
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2588 - g_loss: 0.9897 - d_accuracy: 0.6339
Epoch 247/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2539 - g_loss: 0.9795 - d_accuracy: 0.6479Epoch 247: D Accuracy = 0.7206
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2525 - g_loss: 0.9764 - d_accuracy: 0.6505
Epoch 248/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2412 - g_loss: 0.9927 - d_accuracy: 0.6569Epoch 248: D Accuracy = 0.6691
27/27 [==============================] - 1s 36ms/step - d_loss: 1.2419 - g_loss: 0.9869 - d_accuracy: 0.6573
Epoch 249/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2502 - g_loss: 1.0093 - d_accuracy: 0.6469Epoch 249: D Accuracy = 0.6103
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2493 - g_loss: 1.0167 - d_accuracy: 0.6456
Epoch 250/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2413 - g_loss: 0.9894 - d_accuracy: 0.6494Epoch 250: D Accuracy = 0.6618
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2397 - g_loss: 0.9918 - d_accuracy: 0.6498
Epoch 251/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2552 - g_loss: 0.9882 - d_accuracy: 0.6457Epoch 251: D Accuracy = 0.6618
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2545 - g_loss: 0.9857 - d_accuracy: 0.6463
Epoch 252/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2636 - g_loss: 0.9820 - d_accuracy: 0.6439Epoch 252: D Accuracy = 0.6397
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2625 - g_loss: 0.9825 - d_accuracy: 0.6438
Epoch 253/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2351 - g_loss: 0.9997 - d_accuracy: 0.6601Epoch 253: D Accuracy = 0.6471
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2364 - g_loss: 1.0009 - d_accuracy: 0.6596
Epoch 254/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2559 - g_loss: 0.9860 - d_accuracy: 0.6431Epoch 254: D Accuracy = 0.6838
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2537 - g_loss: 0.9859 - d_accuracy: 0.6445
Epoch 255/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2478 - g_loss: 0.9883 - d_accuracy: 0.6513Epoch 255: D Accuracy = 0.6397
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2466 - g_loss: 0.9894 - d_accuracy: 0.6509
Epoch 256/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2413 - g_loss: 1.0000 - d_accuracy: 0.6634Epoch 256: D Accuracy = 0.6397
27/27 [==============================] - 1s 38ms/step - d_loss: 1.2422 - g_loss: 1.0016 - d_accuracy: 0.6626
Epoch 257/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2463 - g_loss: 0.9860 - d_accuracy: 0.6560Epoch 257: D Accuracy = 0.7353
27/27 [==============================] - 1s 37ms/step - d_loss: 1.2455 - g_loss: 0.9825 - d_accuracy: 0.6588
Epoch 258/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2333 - g_loss: 1.0140 - d_accuracy: 0.6620Epoch 258: D Accuracy = 0.6471
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2335 - g_loss: 1.0150 - d_accuracy: 0.6615
Epoch 259/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2433 - g_loss: 0.9948 - d_accuracy: 0.6523Epoch 259: D Accuracy = 0.6397
27/27 [==============================] - 1s 35ms/step - d_loss: 1.2441 - g_loss: 0.9977 - d_accuracy: 0.6519
Epoch 260/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2421 - g_loss: 0.9945 - d_accuracy: 0.6553Epoch 260: D Accuracy = 0.6544
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2418 - g_loss: 0.9931 - d_accuracy: 0.6553
Epoch 261/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2330 - g_loss: 1.0066 - d_accuracy: 0.6660Epoch 261: D Accuracy = 0.7426
27/27 [==============================] - 1s 34ms/step - d_loss: 1.2304 - g_loss: 1.0049 - d_accuracy: 0.6687
Epoch 262/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2538 - g_loss: 0.9947 - d_accuracy: 0.6391Epoch 262: D Accuracy = 0.6618
27/27 [==============================] - 1s 36ms/step - d_loss: 1.2526 - g_loss: 0.9926 - d_accuracy: 0.6399
Epoch 263/500
27/27 [==============================] - ETA: 0s - d_loss: 1.2356 - g_loss: 0.9962 - d_accuracy: 0.6555Epoch 263: D Accuracy = 0.6838
27/27 [==============================] - 1s 37ms/step - d_loss: 1.2354 - g_loss: 0.9950 - d_accuracy: 0.6565

# === Final Save and Grid ===
def generate_and_save_grid_only(weights_dir, latent_dim=128, images_per_class=10, save_dir="(2)Generated_Grid_160_Final"):
    os.makedirs(save_dir, exist_ok=True)
    all_images = []

    num_classes = 16  # You can change if needed

    for class_idx in range(num_classes):
        print(f"Loading generator weights for class {class_idx}...")

        # Rebuild the generator and load its weights
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(weights_dir, f"(2)final_generator_class_{class_idx}.h5")
        generator.load_weights(weight_path)

        # Generate images
        noise = np.random.normal(0, 1, (images_per_class, latent_dim))
        generated_images = generator.predict(noise)
        generated_images = 0.5 * generated_images + 0.5  # Rescale to [0,1]

        for i in range(images_per_class):
            img = generated_images[i, :, :, 0]
            all_images.append(img)

    # Save a grid of all generated images
    fig, axes = plt.subplots(num_classes, images_per_class, figsize=(images_per_class, num_classes))
    fig.suptitle("DCGAN Generated Grid (Final or Best Weights)", fontsize=16)

    for i, ax in enumerate(axes.flat):
        ax.imshow(all_images[i], cmap='gray')
        ax.axis('off')

    grid_path = os.path.join(save_dir, "(2)new_grid_160images.png")
    plt.tight_layout()
    plt.savefig(grid_path)
    plt.show()

    print(f"Grid of 160 images saved at: {grid_path}")


def plot_loss_history(histories):
    for class_idx, history in histories.items():
        g_loss = history.history['g_loss']
        d_loss = history.history['d_loss']
        d_acc = history.history.get('d_accuracy', None)
        epochs_range = range(1, len(g_loss) + 1)

        plt.figure(figsize=(12, 4))

        plt.subplot(1, 2, 1)
        plt.plot(epochs_range, d_loss, label="Discriminator Loss")
        plt.plot(epochs_range, g_loss, label="Generator Loss")
        plt.title(f"Loss — Class {class_idx}")
        plt.xlabel("Epochs")
        plt.ylabel("Loss")
        plt.legend()
        plt.grid(True)

        if d_acc is not None:
            plt.subplot(1, 2, 2)
            plt.plot(epochs_range, d_acc, label="Discriminator Accuracy", color='green')
            plt.title(f"Discriminator Accuracy — Class {class_idx}")
            plt.xlabel("Epochs")
            plt.ylabel("Accuracy")
            plt.grid(True)
            plt.legend()

        plt.tight_layout()
        plt.show()

        
for class_idx, dcgan in dcgan_models.items():
    gen = dcgan.generator
    weight_path = os.path.join(final_save_dir, f"(2)final_generator_class_{class_idx}.h5")
    gen.save_weights(weight_path)
    print(f"Saved generator weights for class {class_idx} to {weight_path}")

Saved generator weights for class 0 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_0.h5
Saved generator weights for class 1 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_1.h5
Saved generator weights for class 2 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_2.h5
Saved generator weights for class 3 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_3.h5
Saved generator weights for class 4 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_4.h5
Saved generator weights for class 5 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_5.h5
Saved generator weights for class 6 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_6.h5
Saved generator weights for class 7 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_7.h5
Saved generator weights for class 8 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_8.h5
Saved generator weights for class 9 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_9.h5
Saved generator weights for class 10 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_10.h5
Saved generator weights for class 11 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_11.h5
Saved generator weights for class 12 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_12.h5
Saved generator weights for class 13 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_13.h5
Saved generator weights for class 14 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_14.h5
Saved generator weights for class 15 to (2)16Models_saved_generators_epochs(last)\(2)final_generator_class_15.h5

plt.plot(history.history['d_accuracy'])
plt.title("Discriminator Accuracy over Epochs")
plt.xlabel("Epoch")
plt.ylabel("Accuracy")
plt.grid(True)
plt.show()

plot_loss_history(histories)

generate_and_save_grid_only(weights_dir="(2)16Models_saved_generators_epochs(last)")

Loading generator weights for class 0...
1/1 [==============================] - 0s 106ms/step
Loading generator weights for class 1...
1/1 [==============================] - 0s 105ms/step
Loading generator weights for class 2...
1/1 [==============================] - 0s 109ms/step
Loading generator weights for class 3...
1/1 [==============================] - 0s 135ms/step
Loading generator weights for class 4...
1/1 [==============================] - 0s 108ms/step
Loading generator weights for class 5...
1/1 [==============================] - 0s 127ms/step
Loading generator weights for class 6...
1/1 [==============================] - 0s 118ms/step
Loading generator weights for class 7...
1/1 [==============================] - 0s 104ms/step
Loading generator weights for class 8...
1/1 [==============================] - 0s 107ms/step
Loading generator weights for class 9...
1/1 [==============================] - 0s 103ms/step
Loading generator weights for class 10...
1/1 [==============================] - 0s 108ms/step
Loading generator weights for class 11...
1/1 [==============================] - 0s 100ms/step
Loading generator weights for class 12...
1/1 [==============================] - 0s 103ms/step
Loading generator weights for class 13...
1/1 [==============================] - 0s 103ms/step
Loading generator weights for class 14...
1/1 [==============================] - 0s 103ms/step
Loading generator weights for class 15...
1/1 [==============================] - 0s 105ms/step

Grid of 160 images saved at: (2)Generated_Grid_160_Final\(2)new_grid_160images.png

generate_and_save_grid_only(weights_dir="(2)16Models_saved_generators_epochs")

Loading generator weights for class 0...
1/1 [==============================] - 0s 99ms/step
Loading generator weights for class 1...
1/1 [==============================] - 0s 99ms/step
Loading generator weights for class 2...
1/1 [==============================] - 0s 105ms/step
Loading generator weights for class 3...
1/1 [==============================] - 0s 111ms/step
Loading generator weights for class 4...
1/1 [==============================] - 0s 104ms/step
Loading generator weights for class 5...
1/1 [==============================] - 0s 98ms/step
Loading generator weights for class 6...
1/1 [==============================] - 0s 100ms/step
Loading generator weights for class 7...
1/1 [==============================] - 0s 99ms/step
Loading generator weights for class 8...
1/1 [==============================] - 0s 102ms/step
Loading generator weights for class 9...
1/1 [==============================] - 0s 98ms/step
Loading generator weights for class 10...
1/1 [==============================] - 0s 97ms/step
Loading generator weights for class 11...
1/1 [==============================] - 0s 97ms/step
Loading generator weights for class 12...
1/1 [==============================] - 0s 97ms/step
Loading generator weights for class 13...
1/1 [==============================] - 0s 100ms/step
Loading generator weights for class 14...
1/1 [==============================] - 0s 98ms/step
Loading generator weights for class 15...
1/1 [==============================] - 0s 99ms/step

Grid of 160 images saved at: (2)Generated_Grid_160_Final\(2)new_grid_160images.png

def load_dcgan_models_from_weights(weight_dir, latent_dim=128):
    dcgan_models = {}
    for class_idx in range(16):
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(weight_dir, f"(2)best_generator_class_{class_idx}.h5")
        if not os.path.exists(weight_path):  # Try loading final weights if best not found
            weight_path = os.path.join(weight_dir, f"(2)final_generator_class_{class_idx}.h5")
        generator.load_weights(weight_path)

        # Wrap inside dummy DCGAN model structure
        dcgan = tf.keras.models.Model()
        dcgan.generator = generator
        dcgan_models[class_idx] = dcgan
    return dcgan_models

# For final weights
dcgan_models_final = load_dcgan_models_from_weights("(2)16Models_saved_generators_epochs(last)")
generate_word_from_models(dcgan_models_final, "DEEP LEANING")     # Final weights

Skipping ' ' — not trained.

# For final weights
dcgan_models_final = load_dcgan_models_from_weights("(2)16Models_saved_generators_epochs(last)")
generate_word_from_models(dcgan_models_final, "DEEP LEANING")     # Final weights

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[43], line 3
      1 # For final weights
      2 dcgan_models_final = load_dcgan_models_from_weights("(2)16Models_saved_generators_epochs(last)")
----> 3 generate_word_from_models(dcgan_models_final, "DEEP LEANING")

NameError: name 'generate_word_from_models' is not defined

generate_word_from_models(dcgan_models, "DEEPLEARNING", latent_dim=128)
generate_word_from_models(dcgan_models, "DAAA", latent_dim=128)

Skipping 'R' — not trained.

import os
import numpy as np
import matplotlib.pyplot as plt

def save_10_images_per_class(weights_dir, latent_dim=128, images_per_class=10, base_save_dir="MULTIDCGAN_FINAL"):
    os.makedirs(base_save_dir, exist_ok=True)
    num_classes = 16

    for class_idx in range(num_classes):
        print(f"Generating images for class {class_idx}...")

        # Create class-specific folder
        class_dir = os.path.join(base_save_dir, f"class_{class_idx}")
        os.makedirs(class_dir, exist_ok=True)

        # Rebuild generator and load weights
        generator = build_generator(latent_dim, channels=1)
        weight_path = os.path.join(weights_dir, f"(2)final_generator_class_{class_idx}.h5")
        if not os.path.exists(weight_path):
            print(f" Skipping class {class_idx} — file not found: {weight_path}")
            continue

        generator.load_weights(weight_path)

        # Generate images
        noise = np.random.normal(0, 1, (images_per_class, latent_dim))
        generated_images = generator.predict(noise, verbose=0)
        generated_images = 0.5 * generated_images + 0.5  # Rescale from [-1,1] to [0,1]

        # Save each image
        for i in range(images_per_class):
            img = generated_images[i, :, :, 0]  # Grayscale
            img_path = os.path.join(class_dir, f"class{class_idx}_img{i+1}.png")
            plt.imsave(img_path, img, cmap='gray')

        print(f"Saved 10 images to {class_dir}")

    print("All class images saved to MULTIDCGAN_FINAL.")

save_10_images_per_class(weights_dir="(2)16Models_saved_generators_epochs(last)")

Generating images for class 0...
Saved 10 images to MULTIDCGAN_FINAL\class_0
Generating images for class 1...
Saved 10 images to MULTIDCGAN_FINAL\class_1
Generating images for class 2...
Saved 10 images to MULTIDCGAN_FINAL\class_2
Generating images for class 3...
Saved 10 images to MULTIDCGAN_FINAL\class_3
Generating images for class 4...
Saved 10 images to MULTIDCGAN_FINAL\class_4
Generating images for class 5...
Saved 10 images to MULTIDCGAN_FINAL\class_5
Generating images for class 6...
Saved 10 images to MULTIDCGAN_FINAL\class_6
Generating images for class 7...
Saved 10 images to MULTIDCGAN_FINAL\class_7
Generating images for class 8...
Saved 10 images to MULTIDCGAN_FINAL\class_8
Generating images for class 9...
Saved 10 images to MULTIDCGAN_FINAL\class_9
Generating images for class 10...
Saved 10 images to MULTIDCGAN_FINAL\class_10
Generating images for class 11...
Saved 10 images to MULTIDCGAN_FINAL\class_11
Generating images for class 12...
Saved 10 images to MULTIDCGAN_FINAL\class_12
Generating images for class 13...
Saved 10 images to MULTIDCGAN_FINAL\class_13
Generating images for class 14...
Saved 10 images to MULTIDCGAN_FINAL\class_14
Generating images for class 15...
Saved 10 images to MULTIDCGAN_FINAL\class_15
All class images saved to MULTIDCGAN_FINAL.

	24	0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	...	0.552	0.553	0.554	0.555	0.556	0.557	0.558	0.559	0.560	0.561
count	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.00000	55363.000000	...	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000	55363.000000
mean	11.589003	1.787602	1.787602	1.787602	1.787710	1.791991	1.810921	1.819807	1.81009	2.259542	...	1.787602	1.787602	1.787602	1.792479	1.788180	1.787602	1.787602	1.787602	1.787602	1.787602
std	7.467979	17.352303	17.352303	17.352303	17.368913	17.359561	17.449014	17.500948	17.45474	19.855490	...	17.352303	17.352303	17.352303	17.383111	17.352776	17.352303	17.352303	17.352303	17.352303	17.352303
min	-2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	5.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	10.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	16.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.00000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	26.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.00000	255.000000	...	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000	255.000000

Layer	Type	Parameters	Activation	Output Shape
Input	Input	`(latent_dim,)`	–	`(latent_dim,)`
Dense	Fully Connected	Units = `7×7×128`	ReLU	`(6272,)`
Reshape	Reshape	`(7, 7, 128)`	–	`(7, 7, 128)`
UpSampling2D	Upsampling	–	–	`(14, 14, 128)`
Conv2D	Conv	128 filters, 3×3, padding = same	ReLU	`(14, 14, 128)`
BatchNormalization	BN	Default momentum	–	`(14, 14, 128)`
UpSampling2D	Upsampling	–	–	`(28, 28, 128)`
Conv2D	Conv	64 filters, 3×3, padding = same	ReLU	`(28, 28, 64)`
BatchNormalization	BN	Default momentum	–	`(28, 28, 64)`
Conv2D	Conv	`channels` filters, 3×3, padding = same	Tanh	`(28, 28, channels)`

Layer Type	Output Shape	Parameters / Description
Input	(latent_dim,)	Random noise vector
Dense	(7, 7, 256)	Fully connected layer reshaped into 7×7×256
BatchNormalization	(7, 7, 256)	Normalize activations
LeakyReLU (α=0.2)	(7, 7, 256)	Non-linearity
Conv2DTranspose	(14, 14, 128)	Kernel=4, Stride=2, Padding='same'
BatchNormalization	(14, 14, 128)
LeakyReLU (α=0.2)	(14, 14, 128)
Conv2DTranspose	(28, 28, 64)	Kernel=4, Stride=2, Padding='same'
BatchNormalization	(28, 28, 64)
LeakyReLU (α=0.2)	(28, 28, 64)
Conv2DTranspose	(28, 28, 32)	Kernel=3, Stride=1, Padding='same' (refinement layer)
BatchNormalization	(28, 28, 32)
LeakyReLU (α=0.2)	(28, 28, 32)
Conv2D	(28, 28, 1)	Final output layer with 1 channel
Activation (tanh)	(28, 28, 1)	Output scaled to [-1, 1]

Layer Type	Output Shape	Parameters / Description
Input	(28, 28, 1)	Real or generated grayscale image
Conv2D	(14, 14, 64)	Kernel=3, Stride=2, Padding='same'
LeakyReLU (α=0.2)	(14, 14, 64)
Dropout (0.3)	(14, 14, 64)	Regularization
Conv2D	(7, 7, 128)	Kernel=3, Stride=2, Padding='same'
BatchNormalization	(7, 7, 128)
LeakyReLU (α=0.2)	(7, 7, 128)
Dropout (0.3)	(7, 7, 128)
Conv2D	(7, 7, 128)	Kernel=3, Stride=1, Padding='same'
BatchNormalization	(7, 7, 128)
LeakyReLU (α=0.2)	(7, 7, 128)
Flatten	(6272,)	Flatten all features
Dense	(64,)	Intermediate dense layer
LeakyReLU (α=0.2)	(64,)
Dense	(1,)	Sigmoid activation for real/fake prediction

Aspect	Version 1	Version 2
Final Layer Type	`Conv2D` (normal convolution)	`Conv2DTranspose` (transpose convolution)
Final Layer Kernel	3×3 kernel	2×2 kernel
Final Layer Stride	Default stride = 1 (Conv2D)	stride = 1
Final Layer Activation	Separate `Activation('tanh')` after Conv2D	`'tanh'` integrated inside final `Conv2DTranspose`
Upsampling Layers	3 × `Conv2DTranspose` + 1 Conv2D refinement layer	4 × `Conv2DTranspose` (last layer produces output)
Output Refinement	Final refinement via normal Conv2D	Refinement integrated into last `Conv2DTranspose`

Part A: DCGAN 16 MODELS¶

Class-Specific DCGAN Models (One Model per Class)¶

Rationale:¶

Training Setup:¶

Output:¶

Table of Content¶

Table of Contents¶

Imports¶

Control GPU Memory Usage¶

Background Research and Data Exploration¶

Load Dataset¶

Data Exploration¶

Print out sample images by class¶

Filter Out Unwanted Labels and Reset Index¶

Filter Out Unwanted Labels and Reset Index¶

Map Original Labels to a 0–15 Range¶

Image Average¶

Feature Engineering¶

Image Preprocessing Summary¶

Application of Multi-DCGANS¶

Multi-DCGANS 16 Models - Baseline Modelling¶

Baseline DCGAN Generator¶

Baseline DCGAN Discriminator¶

Configuration Parameters¶

Baseline DCGAN Architecture Explanation¶

Instantiate Models — Summary¶

DCGAN Training Class (`DCGANModel`) — Summary¶

Prepare Class-Wise Datasets (`prepare_class_datasets`) — Summary¶

Per-Class DCGAN Training Loop — Summary¶

Generate Classwise Grid — Summary¶

Generate letters¶

Model improvement 1¶

DCGAN Architecture Overview (Enhanced Generator)¶

Generator Architecture¶

Discriminator Architecture (Simplified)¶

Model Improvement 2¶

Differences Between Generator Versions 1 and 2¶

Training Settings (Applied to Both Versions)¶

	24	0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	...	0.552	0.553	0.554	0.555	0.556	0.557	0.558	0.559	0.560	0.561
0	-2	142	142	142	142	142	142	142	142	142	...	142	142	142	142	142	142	142	142	142	142
1	15	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	14	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	-2	120	120	120	120	120	120	120	120	120	...	120	120	120	120	120	120	120	120	120	120
4	-1	131	131	131	131	131	131	131	131	200	...	131	131	131	131	131	131	131	131	131	131

Setting	Value / Choice	Why
Label Smoothing	Real labels = 0.9, Fake = 0.0	Prevents D overconfidence; gives G better gradients when D gets strong
G Optimizer	`Adam(lr=2e-4, beta_1=0.5)`	Slightly faster G to keep up with D; DCGAN-standard β₁ stabilizes momentum
D Optimizer	`Adam(lr=5e-5, beta_1=0.5)`	Slower D to avoid dominating (keeps D-acc ~60–80% “healthy” range)

Component	Layer Type	Parameters / Details
Generator	Dense	256 * 7 * 7 units, input dimension = `latent_dim`
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Reshape	Output shape: (7, 7, 256)
	Conv2DTranspose	128 filters, kernel size = 4, strides = 2, padding = 'same' → output 14x14
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Conv2DTranspose	64 filters, kernel size = 4, strides = 2, padding = 'same' → output 28x28
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Conv2DTranspose	32 filters, kernel size = 3, strides = 1, padding = 'same' (refinement layer)
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Conv2DTranspose	`channels` filters, kernel size = 2, strides = 1, padding = 'same', activation = 'tanh' (output layer)
Discriminator	Conv2D	64 filters, kernel size = 3, strides = 2, padding = 'same', input shape = `img_shape`
	LeakyReLU	Alpha = 0.2
	Dropout	Rate = 0.3
	Conv2D	128 filters, kernel size = 3, strides = 2, padding = 'same'
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Dropout	Rate = 0.3
	Conv2D	128 filters, kernel size = 3, strides = 1, padding = 'same'
	BatchNormalization	Default
	LeakyReLU	Alpha = 0.2
	Flatten	-
	Dense	64 units
	LeakyReLU	Alpha = 0.2
	Dense	1 unit, activation = 'sigmoid' (output layer)

	24	0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	...	0.552	0.553	0.554	0.555	0.556	0.557	0.558	0.559	0.560	0.561
0	-2	142	142	142	142	142	142	142	142	142	...	142	142	142	142	142	142	142	142	142	142
1	15	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	14	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	-2	120	120	120	120	120	120	120	120	120	...	120	120	120	120	120	120	120	120	120	120
4	-1	131	131	131	131	131	131	131	131	200	...	131	131	131	131	131	131	131	131	131	131

Part A: DCGAN 16 MODELS¶

Class-Specific DCGAN Models (One Model per Class)¶

Rationale:¶

Training Setup:¶

Output:¶

Table of Content¶

Table of Contents¶

Imports¶

Control GPU Memory Usage¶

Background Research and Data Exploration¶

Load Dataset¶

Data Exploration¶

Print out sample images by class¶

Filter Out Unwanted Labels and Reset Index¶

Filter Out Unwanted Labels and Reset Index¶

Map Original Labels to a 0–15 Range¶

Image Average¶

Feature Engineering¶

Image Preprocessing Summary¶

Application of Multi-DCGANS¶

Multi-DCGANS 16 Models - Baseline Modelling¶

Baseline DCGAN Generator¶

Baseline DCGAN Discriminator¶

Configuration Parameters¶

Baseline DCGAN Architecture Explanation¶

Instantiate Models — Summary¶

DCGAN Training Class (DCGANModel) — Summary¶

Prepare Class-Wise Datasets (prepare_class_datasets) — Summary¶

Per-Class DCGAN Training Loop — Summary¶

Generate Classwise Grid — Summary¶

Generate letters¶

Model improvement 1¶

DCGAN Architecture Overview (Enhanced Generator)¶

Generator Architecture¶

Discriminator Architecture (Simplified)¶

Model Improvement 2¶

Differences Between Generator Versions 1 and 2¶

Training Settings (Applied to Both Versions)¶

DCGAN Training Class (`DCGANModel`) — Summary¶

Prepare Class-Wise Datasets (`prepare_class_datasets`) — Summary¶

	24	0	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	...	0.552	0.553	0.554	0.555	0.556	0.557	0.558	0.559	0.560	0.561
0	-2	142	142	142	142	142	142	142	142	142	...	142	142	142	142	142	142	142	142	142	142
1	15	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	14	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	-2	120	120	120	120	120	120	120	120	120	...	120	120	120	120	120	120	120	120	120	120
4	-1	131	131	131	131	131	131	131	131	200	...	131	131	131	131	131	131	131	131	131	131